{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using gpu device 0: Tesla K80 (CNMeM is disabled, cuDNN 5103)\n",
      "/home/ubuntu/anaconda2/lib/python2.7/site-packages/theano/sandbox/cuda/__init__.py:600: UserWarning: Your cuDNN version is more recent than the one Theano officially supports. If you see any problems, try updating Theano or downgrading cuDNN to version 5.\n",
      "  warnings.warn(warn)\n"
     ]
    }
   ],
   "source": [
    "from theano.sandbox import cuda"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using Theano backend.\n"
     ]
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import utils; reload(utils)\n",
    "from utils import *\n",
    "from __future__ import division, print_function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#path = \"data/ml-20m/\"\n",
    "path = \"data/ml-small/\"\n",
    "model_path = path + 'models/'\n",
    "if not os.path.exists(model_path): os.mkdir(model_path)\n",
    "batch_size=64"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "## Set up data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "We're working with the movielens data, which contains one rating per row, like this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false,
    "hidden": true,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>userId</th>\n",
       "      <th>movieId</th>\n",
       "      <th>rating</th>\n",
       "      <th>timestamp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>31</td>\n",
       "      <td>2.5</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
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       "    <tr>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
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       "      <td>4.0</td>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   userId  movieId  rating   timestamp\n",
       "0       1       31     2.5  1260759144\n",
       "1       1     1029     3.0  1260759179\n",
       "2       1     1061     3.0  1260759182\n",
       "3       1     1129     2.0  1260759185\n",
       "4       1     1172     4.0  1260759205"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ratings = pd.read_csv(path+'ratings.csv')\n",
    "ratings.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false,
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "100004"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(ratings)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "Just for display purposes, let's read in the movie names too."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "outputs": [],
   "source": [
    "movie_names = pd.read_csv(path+'movies.csv').set_index('movieId')['title'].to_dict()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "outputs": [],
   "source": [
    "users = ratings.userId.unique()\n",
    "movies = ratings.movieId.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false,
    "hidden": true
   },
   "outputs": [],
   "source": [
    "userid2idx = {o:i for i,o in enumerate(users)}\n",
    "movieid2idx = {o:i for i,o in enumerate(movies)}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "We update the movie and user ids so that they are contiguous integers, which we want when using embeddings."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "outputs": [],
   "source": [
    "ratings.movieId = ratings.movieId.apply(lambda x: movieid2idx[x])\n",
    "ratings.userId = ratings.userId.apply(lambda x: userid2idx[x])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false,
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, 670, 0, 9065)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "user_min, user_max, movie_min, movie_max = (ratings.userId.min(), \n",
    "    ratings.userId.max(), ratings.movieId.min(), ratings.movieId.max())\n",
    "user_min, user_max, movie_min, movie_max"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false,
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(671, 9066)"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n_users = ratings.userId.nunique()\n",
    "n_movies = ratings.movieId.nunique()\n",
    "n_users, n_movies"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "This is the number of latent factors in each embedding."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "outputs": [],
   "source": [
    "n_factors = 50"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "outputs": [],
   "source": [
    "np.random.seed = 42"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "Randomly split into training and validation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "outputs": [],
   "source": [
    "msk = np.random.rand(len(ratings)) < 0.8\n",
    "trn = ratings[msk]\n",
    "val = ratings[~msk]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "## Create subset for Excel"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "We create a crosstab of the most popular movies and most movie-addicted users which we'll copy into Excel for creating a simple example. This isn't necessary for any of the modeling below however."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "collapsed": false,
    "hidden": true
   },
   "outputs": [],
   "source": [
    "g=ratings.groupby('userId')['rating'].count()\n",
    "topUsers=g.sort_values(ascending=False)[:15]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "collapsed": false,
    "hidden": true
   },
   "outputs": [],
   "source": [
    "g=ratings.groupby('movieId')['rating'].count()\n",
    "topMovies=g.sort_values(ascending=False)[:15]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "collapsed": false,
    "hidden": true
   },
   "outputs": [],
   "source": [
    "top_r = ratings.join(topUsers, rsuffix='_r', how='inner', on='userId')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "collapsed": false,
    "hidden": true
   },
   "outputs": [],
   "source": [
    "top_r = top_r.join(topMovies, rsuffix='_r', how='inner', on='movieId')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "collapsed": false,
    "hidden": true,
    "scrolled": true
   },
   "outputs": [
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       "      <td>4.0</td>\n",
       "      <td>4.5</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.5</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>623</th>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "movieId  27   49   57   72   79   89   92   99   143  179  180  197  402  417  \\\n",
       "userId                                                                          \n",
       "14       3.0  5.0  1.0  3.0  4.0  4.0  5.0  2.0  5.0  5.0  4.0  5.0  5.0  2.0   \n",
       "29       5.0  5.0  5.0  4.0  5.0  4.0  4.0  5.0  4.0  4.0  5.0  5.0  3.0  4.0   \n",
       "72       4.0  5.0  5.0  4.0  5.0  3.0  4.5  5.0  4.5  5.0  5.0  5.0  4.5  5.0   \n",
       "211      5.0  4.0  4.0  3.0  5.0  3.0  4.0  4.5  4.0  NaN  3.0  3.0  5.0  3.0   \n",
       "212      2.5  NaN  2.0  5.0  NaN  4.0  2.5  NaN  5.0  5.0  3.0  3.0  4.0  3.0   \n",
       "293      3.0  NaN  4.0  4.0  4.0  3.0  NaN  3.0  4.0  4.0  4.5  4.0  4.5  4.0   \n",
       "310      3.0  3.0  5.0  4.5  5.0  4.5  2.0  4.5  4.0  3.0  4.5  4.5  4.0  3.0   \n",
       "379      5.0  5.0  5.0  4.0  NaN  4.0  5.0  4.0  4.0  4.0  NaN  3.0  5.0  4.0   \n",
       "451      4.0  5.0  4.0  5.0  4.0  4.0  5.0  5.0  4.0  4.0  4.0  4.0  2.0  3.5   \n",
       "467      3.0  3.5  3.0  2.5  NaN  NaN  3.0  3.5  3.5  3.0  3.5  3.0  3.0  4.0   \n",
       "508      5.0  5.0  4.0  3.0  5.0  2.0  4.0  4.0  5.0  5.0  5.0  3.0  4.5  3.0   \n",
       "546      NaN  5.0  2.0  3.0  5.0  NaN  5.0  5.0  NaN  2.5  2.0  3.5  3.5  3.5   \n",
       "563      1.0  5.0  3.0  5.0  4.0  5.0  5.0  NaN  2.0  5.0  5.0  3.0  3.0  4.0   \n",
       "579      4.5  4.5  3.5  3.0  4.0  4.5  4.0  4.0  4.0  4.0  3.5  3.0  4.5  4.0   \n",
       "623      NaN  5.0  3.0  3.0  NaN  3.0  5.0  NaN  5.0  5.0  5.0  5.0  2.0  5.0   \n",
       "\n",
       "movieId  505  \n",
       "userId        \n",
       "14       5.0  \n",
       "29       5.0  \n",
       "72       4.0  \n",
       "211      NaN  \n",
       "212      2.0  \n",
       "293      NaN  \n",
       "310      4.0  \n",
       "379      4.0  \n",
       "451      5.0  \n",
       "467      4.0  \n",
       "508      4.5  \n",
       "546      5.0  \n",
       "563      5.0  \n",
       "579      4.5  \n",
       "623      4.0  "
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(top_r.userId, top_r.movieId, top_r.rating, aggfunc=np.sum)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "heading_collapsed": true
   },
   "source": [
    "## Dot product"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "The most basic model is a dot product of a movie embedding and a user embedding. Let's see how well that works:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "outputs": [],
   "source": [
    "user_in = Input(shape=(1,), dtype='int64', name='user_in')\n",
    "u = Embedding(n_users, n_factors, input_length=1, W_regularizer=l2(1e-4))(user_in)\n",
    "movie_in = Input(shape=(1,), dtype='int64', name='movie_in')\n",
    "m = Embedding(n_movies, n_factors, input_length=1, W_regularizer=l2(1e-4))(movie_in)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false,
    "hidden": true
   },
   "outputs": [],
   "source": [
    "x = merge([u, m], mode='dot')\n",
    "x = Flatten()(x)\n",
    "model = Model([user_in, movie_in], x)\n",
    "model.compile(Adam(0.001), loss='mse')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 80010 samples, validate on 19994 samples\n",
      "Epoch 1/1\n",
      "80010/80010 [==============================] - 1s - loss: 9.8328 - val_loss: 3.7591\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x7f6fb5b0b590>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit([trn.userId, trn.movieId], trn.rating, batch_size=64, nb_epoch=1, \n",
    "          validation_data=([val.userId, val.movieId], val.rating))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "outputs": [],
   "source": [
    "model.optimizer.lr=0.01"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 80010 samples, validate on 19994 samples\n",
      "Epoch 1/3\n",
      "80010/80010 [==============================] - 2s - loss: 3.1172 - val_loss: 2.0018\n",
      "Epoch 2/3\n",
      "80010/80010 [==============================] - 1s - loss: 2.4130 - val_loss: 1.6529\n",
      "Epoch 3/3\n",
      "80010/80010 [==============================] - 1s - loss: 2.2526 - val_loss: 1.5249\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x7f6fb5b0ba90>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit([trn.userId, trn.movieId], trn.rating, batch_size=64, nb_epoch=3, \n",
    "          validation_data=([val.userId, val.movieId], val.rating))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "outputs": [],
   "source": [
    "model.optimizer.lr=0.001"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false,
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 80010 samples, validate on 19994 samples\n",
      "Epoch 1/6\n",
      "80010/80010 [==============================] - 1s - loss: 2.1931 - val_loss: 1.4808\n",
      "Epoch 2/6\n",
      "80010/80010 [==============================] - 1s - loss: 2.1621 - val_loss: 1.4582\n",
      "Epoch 3/6\n",
      "80010/80010 [==============================] - 1s - loss: 2.1386 - val_loss: 1.4439\n",
      "Epoch 4/6\n",
      "80010/80010 [==============================] - 1s - loss: 2.1143 - val_loss: 1.4423\n",
      "Epoch 5/6\n",
      "80010/80010 [==============================] - 1s - loss: 2.0909 - val_loss: 1.4441\n",
      "Epoch 6/6\n",
      "80010/80010 [==============================] - 1s - loss: 2.0673 - val_loss: 1.4457\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x7f6fb5b0b650>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit([trn.userId, trn.movieId], trn.rating, batch_size=64, nb_epoch=6, \n",
    "          validation_data=([val.userId, val.movieId], val.rating))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "The [best benchmarks](http://www.librec.net/example.html) are a bit over 0.9, so this model doesn't seem to be working that well..."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "##  Bias"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "The problem is likely to be that we don't have bias terms - that is, a single bias for each user and each movie representing how positive or negative each user is, and how good each movie is. We can add that easily by simply creating an embedding with one output for each movie and each user, and adding it to our output."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "outputs": [],
   "source": [
    "def embedding_input(name, n_in, n_out, reg):\n",
    "    inp = Input(shape=(1,), dtype='int64', name=name)\n",
    "    return inp, Embedding(n_in, n_out, input_length=1, W_regularizer=l2(reg))(inp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "outputs": [],
   "source": [
    "user_in, u = embedding_input('user_in', n_users, n_factors, 1e-4)\n",
    "movie_in, m = embedding_input('movie_in', n_movies, n_factors, 1e-4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "outputs": [],
   "source": [
    "def create_bias(inp, n_in):\n",
    "    x = Embedding(n_in, 1, input_length=1)(inp)\n",
    "    return Flatten()(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "outputs": [],
   "source": [
    "ub = create_bias(user_in, n_users)\n",
    "mb = create_bias(movie_in, n_movies)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": false,
    "hidden": true
   },
   "outputs": [],
   "source": [
    "x = merge([u, m], mode='dot')\n",
    "x = Flatten()(x)\n",
    "x = merge([x, ub], mode='sum')\n",
    "x = merge([x, mb], mode='sum')\n",
    "model = Model([user_in, movie_in], x)\n",
    "model.compile(Adam(0.001), loss='mse')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 418,
   "metadata": {
    "collapsed": false,
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x7ff4ef2dba90>"
      ]
     },
     "execution_count": 418,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit([trn.userId, trn.movieId], trn.rating, batch_size=64, nb_epoch=1, \n",
    "          validation_data=([val.userId, val.movieId], val.rating))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 419,
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "outputs": [],
   "source": [
    "model.optimizer.lr=0.01"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 420,
   "metadata": {
    "collapsed": false,
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x7ff4feeffa10>"
      ]
     },
     "execution_count": 420,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit([trn.userId, trn.movieId], trn.rating, batch_size=64, nb_epoch=6, \n",
    "          validation_data=([val.userId, val.movieId], val.rating))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 421,
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "outputs": [],
   "source": [
    "model.optimizer.lr=0.001"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 422,
   "metadata": {
    "collapsed": false,
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x7ff4ef0b6510>"
      ]
     },
     "execution_count": 422,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit([trn.userId, trn.movieId], trn.rating, batch_size=64, nb_epoch=10, \n",
    "          validation_data=([val.userId, val.movieId], val.rating))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 248,
   "metadata": {
    "collapsed": false,
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 79838 samples, validate on 20166 samples\n",
      "Epoch 1/5\n",
      "79838/79838 [==============================] - 2s - loss: 0.6237 - val_loss: 0.8030\n",
      "Epoch 2/5\n",
      "79838/79838 [==============================] - 2s - loss: 0.6084 - val_loss: 0.8016\n",
      "Epoch 3/5\n",
      "79838/79838 [==============================] - 2s - loss: 0.5950 - val_loss: 0.8004\n",
      "Epoch 4/5\n",
      "79838/79838 [==============================] - 2s - loss: 0.5844 - val_loss: 0.7981\n",
      "Epoch 5/5\n",
      "79838/79838 [==============================] - 2s - loss: 0.5747 - val_loss: 0.7979\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x7ff508f31190>"
      ]
     },
     "execution_count": 248,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit([trn.userId, trn.movieId], trn.rating, batch_size=64, nb_epoch=5, \n",
    "          validation_data=([val.userId, val.movieId], val.rating))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "This result is quite a bit better than the best benchmarks that we could find with a quick google search - so looks like a great approach!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "outputs": [],
   "source": [
    "model.save_weights(model_path+'bias.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "outputs": [],
   "source": [
    "model.load_weights(model_path+'bias.h5')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "We can use the model to generate predictions by passing a pair of ints - a user id and a movie id. For instance, this predicts that user #3 would really enjoy movie #6."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": false,
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 4.8307]], dtype=float32)"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict([np.array([3]), np.array([6])])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "## Analyze results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "To make the analysis of the factors more interesting, we'll restrict it to the top 2000 most popular movies."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "outputs": [],
   "source": [
    "g=ratings.groupby('movieId')['rating'].count()\n",
    "topMovies=g.sort_values(ascending=False)[:2000]\n",
    "topMovies = np.array(topMovies.index)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "First, we'll look at the movie bias term. We create a 'model' - which in keras is simply a way of associating one or more inputs with one more more outputs, using the functional API. Here, our input is the movie id (a single id), and the output is the movie bias (a single float)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": false,
    "hidden": true
   },
   "outputs": [],
   "source": [
    "get_movie_bias = Model(movie_in, mb)\n",
    "movie_bias = get_movie_bias.predict(topMovies)\n",
    "movie_ratings = [(b[0], movie_names[movies[i]]) for i,b in zip(topMovies,movie_bias)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "Now we can look at the top and bottom rated movies. These ratings are corrected for different levels of reviewer sentiment, as well as different types of movies that different reviewers watch."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false,
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(-0.80693638, 'Battlefield Earth (2000)'),\n",
       " (-0.52972311, 'Spice World (1997)'),\n",
       " (-0.44621521, 'Police Academy 4: Citizens on Patrol (1987)'),\n",
       " (-0.44280463, 'Super Mario Bros. (1993)'),\n",
       " (-0.44120365, 'Police Academy 5: Assignment: Miami Beach (1988)'),\n",
       " (-0.43341145, '2 Fast 2 Furious (Fast and the Furious 2, The) (2003)'),\n",
       " (-0.41436779, 'Howard the Duck (1986)'),\n",
       " (-0.39790833, 'Blade: Trinity (2004)'),\n",
       " (-0.36255124, 'Jaws 3-D (1983)'),\n",
       " (-0.2954753, 'Speed 2: Cruise Control (1997)'),\n",
       " (-0.22500136, 'Scooby-Doo (2002)'),\n",
       " (-0.19756798,\n",
       "  'Highlander III: The Sorcerer (a.k.a. Highlander: The Final Dimension) (1994)'),\n",
       " (-0.19622488, 'Honey, I Blew Up the Kid (1992)'),\n",
       " (-0.17063878, 'Police Academy 6: City Under Siege (1989)'),\n",
       " (-0.16920321, 'Wild Wild West (1999)')]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted(movie_ratings, key=itemgetter(0))[:15]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false,
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(1.7250408, 'Little Big Man (1970)'),\n",
       " (1.7194961, 'Shawshank Redemption, The (1994)'),\n",
       " (1.7044764, \"Howl's Moving Castle (Hauru no ugoku shiro) (2004)\"),\n",
       " (1.7008729, 'Mister Roberts (1955)'),\n",
       " (1.6823107, 'Persuasion (1995)'),\n",
       " (1.6384603, 'Godfather, The (1972)'),\n",
       " (1.6032888, 'Pianist, The (2002)'),\n",
       " (1.5974605, 'Sunset Blvd. (a.k.a. Sunset Boulevard) (1950)'),\n",
       " (1.5918087, 'Jean de Florette (1986)'),\n",
       " (1.5875192, 'Rush (2013)'),\n",
       " (1.5869737, 'Smoke (1995)'),\n",
       " (1.5847712, 'It Happened One Night (1934)'),\n",
       " (1.580586, 'Swingers (1996)'),\n",
       " (1.5783617, 'You Can Count on Me (2000)'),\n",
       " (1.5776982, \"Schindler's List (1993)\")]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted(movie_ratings, key=itemgetter(0), reverse=True)[:15]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "We can now do the same thing for the embeddings."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": false,
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2000, 50)"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_movie_emb = Model(movie_in, m)\n",
    "movie_emb = np.squeeze(get_movie_emb.predict([topMovies]))\n",
    "movie_emb.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "Because it's hard to interpret 50 embeddings, we use [PCA](https://plot.ly/ipython-notebooks/principal-component-analysis/) to simplify them down to just 3 vectors. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "outputs": [],
   "source": [
    "from sklearn.decomposition import PCA\n",
    "pca = PCA(n_components=3)\n",
    "movie_pca = pca.fit(movie_emb.T).components_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "outputs": [],
   "source": [
    "fac0 = movie_pca[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "outputs": [],
   "source": [
    "movie_comp = [(f, movie_names[movies[i]]) for f,i in zip(fac0, topMovies)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "Here's the 1st component. It seems to be 'critically acclaimed' or 'classic'."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false,
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(0.08036764, 'Wallace & Gromit: The Wrong Trousers (1993)'),\n",
       " (0.079808839, 'Wallace & Gromit: A Close Shave (1995)'),\n",
       " (0.077548273, 'American Beauty (1999)'),\n",
       " (0.077498004, 'Usual Suspects, The (1995)'),\n",
       " (0.076784886, 'Pulp Fiction (1994)'),\n",
       " (0.075913154, 'Godfather, The (1972)'),\n",
       " (0.075235315, 'Lord of the Rings: The Fellowship of the Ring, The (2001)'),\n",
       " (0.073968366, '2001: A Space Odyssey (1968)'),\n",
       " (0.073819056, 'Toy Story (1995)'),\n",
       " (0.073727958, \"Ferris Bueller's Day Off (1986)\")]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted(movie_comp, key=itemgetter(0), reverse=True)[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false,
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(-0.058417581, 'Anaconda (1997)'),\n",
       " (-0.046019286, 'Ace Ventura: When Nature Calls (1995)'),\n",
       " (-0.040529612, 'Judge Dredd (1995)'),\n",
       " (-0.038660962, 'Son in Law (1993)'),\n",
       " (-0.038041789, 'RoboCop 3 (1993)'),\n",
       " (-0.036584113, 'Grumpier Old Men (1995)'),\n",
       " (-0.035289925, 'Eraser (1996)'),\n",
       " (-0.034082226, 'Grease 2 (1982)'),\n",
       " (-0.033974353, 'Jack (1996)'),\n",
       " (-0.033512354, 'Police Academy 3: Back in Training (1986)')]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted(movie_comp, key=itemgetter(0))[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "outputs": [],
   "source": [
    "fac1 = movie_pca[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "outputs": [],
   "source": [
    "movie_comp = [(f, movie_names[movies[i]]) for f,i in zip(fac1, topMovies)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "The 2nd is 'hollywood blockbuster'."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false,
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(0.060503632, 'Harold and Maude (1971)'),\n",
       " (0.054804463, 'Brokeback Mountain (2005)'),\n",
       " (0.052384187, 'City Lights (1931)'),\n",
       " (0.051685318, 'Annie Hall (1977)'),\n",
       " (0.051254459, 'Clockwork Orange, A (1971)'),\n",
       " (0.045334909, \"Rosemary's Baby (1968)\"),\n",
       " (0.044675902, 'Deer Hunter, The (1978)'),\n",
       " (0.042716123, 'Spirited Away (Sen to Chihiro no kamikakushi) (2001)'),\n",
       " (0.041788731, 'Bringing Up Baby (1938)'),\n",
       " (0.041603483, 'Easy Rider (1969)')]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted(movie_comp, key=itemgetter(0), reverse=True)[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false,
    "hidden": true,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(-0.094236061, 'Independence Day (a.k.a. ID4) (1996)'),\n",
       " (-0.094174929, 'Titanic (1997)'),\n",
       " (-0.091568619, 'Jurassic Park (1993)'),\n",
       " (-0.09122292, 'Armageddon (1998)'),\n",
       " (-0.089603841, 'Ghost (1990)'),\n",
       " (-0.088665053, 'Shrek (2001)'),\n",
       " (-0.087736182, 'Lord of the Rings: The Two Towers, The (2002)'),\n",
       " (-0.087566942, 'Gladiator (2000)'),\n",
       " (-0.085383952, 'Speed (1994)'),\n",
       " (-0.082565956, 'Star Trek IV: The Voyage Home (1986)')]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted(movie_comp, key=itemgetter(0))[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "outputs": [],
   "source": [
    "fac2 = movie_pca[2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "outputs": [],
   "source": [
    "movie_comp = [(f, movie_names[movies[i]]) for f,i in zip(fac2, topMovies)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "The 3rd is 'violent vs happy'."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false,
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(0.098294519, 'Seven (a.k.a. Se7en) (1995)'),\n",
       " (0.08536391, 'American Psycho (2000)'),\n",
       " (0.079629868,\n",
       "  'L\\xc3\\xa9on: The Professional (a.k.a. The Professional) (L\\xc3\\xa9on) (1994)'),\n",
       " (0.077939048, 'Goonies, The (1985)'),\n",
       " (0.077908911, 'Pulp Fiction (1994)'),\n",
       " (0.076904327, 'Fight Club (1999)'),\n",
       " (0.075976059, 'American Pie (1999)'),\n",
       " (0.075964108, 'Cook the Thief His Wife & Her Lover, The (1989)'),\n",
       " (0.075873129, 'Natural Born Killers (1994)'),\n",
       " (0.074641898, 'Twelve Monkeys (a.k.a. 12 Monkeys) (1995)')]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted(movie_comp, key=itemgetter(0), reverse=True)[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false,
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(-0.10425235, 'Babe (1995)'),\n",
       " (-0.099427424, 'Sleepless in Seattle (1993)'),\n",
       " (-0.096679159, 'Beauty and the Beast (1991)'),\n",
       " (-0.078917101, 'Finding Nemo (2003)'),\n",
       " (-0.072881654, 'Chicken Run (2000)'),\n",
       " (-0.072588563, 'Sabrina (1995)'),\n",
       " (-0.0725106, 'Sling Blade (1996)'),\n",
       " (-0.069847211, 'While You Were Sleeping (1995)'),\n",
       " (-0.067207195, 'Wizard of Oz, The (1939)'),\n",
       " (-0.06691134, 'Snow White and the Seven Dwarfs (1937)')]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted(movie_comp, key=itemgetter(0))[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "We can draw a picture to see how various movies appear on the map of these components. This picture shows the 1st and 3rd components."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 401,
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "outputs": [],
   "source": [
    "import sys\n",
    "stdout, stderr = sys.stdout, sys.stderr # save notebook stdout and stderr\n",
    "reload(sys)\n",
    "sys.setdefaultencoding('utf-8')\n",
    "sys.stdout, sys.stderr = stdout, stderr # restore notebook stdout and stderr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 410,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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YOvL8nKNMmOtPx6c/Qke35IbVf+t8Qk9tZugbf/HSD0EMef1PrBzL//6X+T36fBCFBXnq\nMoUFeRzfMJcBU35h7OeHyMtOY8eSV9X783OzqNO8L76fbGfi16eo3/Yp1n8zipTYyxWeQ4e6LYkK\nOlRhmejgIzjUaVFhmbsd8PuEC/t/p8+k+Uz67jzthr7LjiWvcvVsyc+OgrxsVn0+ED1DM8bNOcbw\nd9YQE3KMrQs1b/CjLx0hOTqIUZ9sY+gbf3HZfyOntv5Y6hjirvhrnK+yj6F5ufvLU1SQp76ut+jo\nGRB3xV89k6aoMA9tHf27yhgSfemIxrbtiybToN0IXBt2rqjLk0CXKg9UiHuQ4IAQQghRgfDAfSRG\nXmDom37YezTHws6DTiM/wdzWncCDfwHgv/kHXBt1puOID7G0r0vDjs/QetAbnNiomZugdtOeNO/z\nMhZ2HrToOxlL+zqEX9in3m9q6YClQ8VPRSur9cCp1GneD0v7OnTx/YyczOskhJ8H4OzOxdi5e9Pl\n2ZlYOXpi49qIgZN/IfaKP3FXT1eq/cKCPI6snU1+zg3cm3RXb9fRM2TA5CVYOzfAxqUhqfFXuHJ6\nC/1eWoBz/fbYuDRk0KvLyMtO5+IhP7S0ddRPlQ2MLTE2t0VLW4eTm76jQfuRtBrwPyzsPHCo25Le\nE+cRcmI92RnJ5GdnkJedTt0W/bGwdcfK0ZOGHUZSy9ELKJkJYFe7GfYezTGzdsG1YSfqty07h1f4\nhT0kR11k8NTfsa/tg71HMwZP/Y34sDOEB96+PsXFRQx8dSk2Lg1xrNcanx4TNfa7Ne5Ko06+WDl6\nYuXoSc/x36Kjq8/Vc5V/xz49ORIjM5tSrxS07P8qbo26YG7tikuDjnQd/TmXjv1dqTYPrpzB8Q1f\n8+z0nTjXb19uOS1tHTqN/Bh7j2aYW7tSv+1T+PR6gaAjqyo9/qpo3GU0DTs+U/KdGjUDLW0ddab6\noMMlffZ/ZRE2Lg2xcvSkz6Qfyc/N5MqZrZVqv7i4iID9v5MceRF379uf0eKiAga9thw796ZYOzeg\nqCCPc7uW0O25L/Dw6UMtRy/6TvoJI3NbTu9YiKIoGJvboigKeoZmGJvboqOrX6nvUUZSJLW9e2Dl\nUA8LOw88Ww1Wv4KTkRyFlUNdnOu3x6yWM06ebWjSdUyZx5ISF1r29ygng4uH/NTlVMVF9HnhB+w9\nmmPj2ojWg94g8uJB9X5btyb49HwBa+cGWNh50G7Yu9i5+3DpeMWvr5tYOnAjJQZVcXG5ZdKTIzGx\ndLj3hbmpIC8b/y0/0O+lhdT27om5jRsNO4zEu/t4zuxYCJQ8bS/Iz2bQa8uwdm6AS4OO9H3pZ0JO\nrCctIUzdlr6ROX0m/UQtRy/cvXtQv+1ThAfu1TwGK0eKCwvITI29xzFU/bWR2k17EbDvN/V1j7t6\nmvN7l1NcWEBORrK6zKmt80mJvYxKpeJawG4un9xAVmq8up1zu5eSlniNzqM+vVeXsYDTnJH6ci8n\nqpW8ViCEEEJUICHsLAW5WfwwUfMXxsKCPNISrwGQHHOJui00p+U612/Pkb8/Jz83Ez0DEwBsXZto\nlDGxdCQ7I1H971vTiauDjWtj9d9vvSOdnV7SV3zYGSKDDvHtWM337hUU0hLCKnz6t/nH8Wz5aSIF\n+TkYGFvQfeyX1G7a63a/Lg3R0r7960Vy9CW0tLRxvGNqvb6RGTaujbkeHVxuP/HXzpAWH0bw0dXq\nbSqVSj1Gx3qtadxlDKtmDcCtSTfcGnejftun1KtINO/9Iuu/8SX+6mncvXtQt8UAXBt2KrOv6zEh\nmFg6aqxAYWFbGxMrR65HB+PeuBsA5jau6msJYGLlQHZ6kvrf2RlJHFw5nciLB8lKT0RVXERhQW6V\nlr0szM8tMyFbeOA+jm+Yy/WYEPKy01EVF1FUmE9mWnyFeRpObSm5oX5+9hEs7evcs/+zOxdzft9y\nMpIiKczPoaioQD0lvbrd+RnV0tLG0MxG/RlNuHaWtMRrpT6jBfk5pMWHUZGDftM5vHomhQV56Ojq\n02bIW/j0fEG937SWE0Zm1up/pyaEUVxUiJNXW/U2RUsLJ8+2FX9GK/E9ajVwKlsXvMSF/b/j1rg7\nXm2HqgNYTbqOZdWs/iya2oja3j2p06wvHs36lplr4npMSKW+R9q6+hqvXZhYOlBUmE9uVhoGxhYU\n5GVzeM1Mrp7ZRmZqPMVFBRQW5GHrpvmz6W66eoagUlFYkFtuMsbC/JwqJRNMjg6msCCX1V8M0the\nXFSIua17yXHHhmDr2kSjTyfPdiiKFsnRwVjYeQBg7Vxf47yZWDoSd0Uzh4GungEqVBXOPCnMzyk1\nA6AyOgz/gKz0RP78uCsqVTHGFvY06TqGExu/Uc9m6jnuG7YvnsySN31QFC0s7T1o0u15LtycmXM9\nNoSDK6fz3Mx96joVyKEkxYYBkF3lAQtRDgkOCCGEEBW49YveczP3wl3Lc+kZmlWihdu/sGrpaP7f\nrqIoFT6JexBlZTm/lbhQpSqmbov+dB/7ZaljMjKvOOdAtzFz8GjaCz1DM40brFuqtAJABYnKVMXF\nePcYT+uBU0uN8dbrAQMmL6bVwP9x7dwOrpzewsGV0xn+7t/U9u6Jh08fJi+4QtjZHYRf2Mffc4bi\n1XY4AyYvrvz4Sgap/tvd51RBQaW6ff02/ziB7Ixkeo7/BjNrV3R09VnxWV+KCvMr3ZuRaS1yM9M0\ntqUnR/L3nGH49HyBzs/MwNDUiviws2yaN/aebTs36EDYuZ0EHV5FhxEfVFg2+Oga9vz2Dt2f/won\nz7boG5pyevsCLvtvqvT4q6L0+UR9PlWqYuzcfRjyxp+lrr/BzRwP5Wk1cCpNu49DR9+ozMBJtX1G\nK/E96vj0RzTq5EvYuR2EndvJkb9n0efFn/DuOhb72j688lMo187vIuLCXjb/9AJ27t6M+riKr5Er\nd35GS/+MuTVWoCSHQ8Buuo/5Ekv7uujqG/LPjxMouuO1i7LkZKaio2dQ4SoNRqbWVXqF5taYRry/\nXuMd/5LjqMQqDUoF301F87sJJcegoJT5c+sWI1NrcrMqfwy36OgZ0P/lhfR98Sey0hIwsXTg3K4l\n6BmaqvPCGJlZ89TbqykqzCfnxnVMLB3Y/9eHWNjWBiD28klyblznlzd91O0WFxcRFXSIs7uW8NYf\nqXcmeLUCct9fnSeBAVGtJDgghBCixkpKSiI8PJzc3PKfJNnXbkZWegKgYGFXu8wy1k71iQ45qrEt\nKvgIprWc0TN48pbLs6/djEvH1mJm7VrpTO23GJvbqZ/WVYa1c31UxcXEXD6OS/0OQEmCx6TIQLy7\nPV/hGJOjgtW/OJfH1rUxtq6NaTP4LVbPHkzg/j+p7d0TAEMTKxp18qVRJ188fHqz6Yex9H3xp1Ir\nKFg71SczNZb05EjMrV2BksRtmSmxWLs0rPSxRocco9f479QJx7LSEshKjat0fQC72j5kZySRk5mi\nTnQYf/UMxUUF9Hh+rvpmL/TU5kq1Z+/RjNYDp+I3sx8oCh2GTyt//JeO4ujZRiNhW2r81SqNv7rY\n125G8JE1GJrUUieeqyxDU6sqfUYt7TzQ0tEl+tIx9eft1me2ouUkK/s9srSvQ4u+k2nRdzI7fnmN\ngD3L8O46FihJnujVZihebYbSuOsYfv+wE6nxV0olXazwe9R9XKWPNTrkGI07P4fnzWUJC/NzSYsP\nw8qh4lwnSVEXsavdrMIytu5NSa5gpsXdrJ0boKOjT3piRLnv11s71efCvt/Jz81S/yyNDjkKKhXW\nTvUr3ReUJDY0tXIqlcRV4xhqV+0Y7qalpa2eqRV0dE2Z+T20dfRuzugoIOTEehq0L8lt4tl6CA51\nW2qU3fLTC1g51KPdU+/d/XOrMXDmvgcqRDnkPRUhhBA1kp/fKtzc6tOr18usWbOOqIirJIQHaPxJ\nT4rA3bsHTl7tWDt3BGHndpCWGE7M5eMcWj2T6EslAYFWg14nKugQh9fMIiUulIuH/PDfMo+2Qyqf\n5R1g/4qP8JtZfjbw6tK8z8vkZWew4dtnib3iT1riNcID9rB98WTyc7OqtS9L+7rUbTmQHYunEHXp\nCImRgfwzfxz6RuY07Diq3Hpthr5N3BV/dix5lYTw86TGXy1ZvnFxyVKSaYnh7F/xETGXj5OeHElE\n4H6SIgLVN/OHVn/GZf9NpMZfITk6mJAT67G08yhzaUV37x7YuDbmnx/GER92hrirp/ln/njs67TA\nrVHlc35ZOdTj4qEVJEcHE3flFBvnjUW7ipnb7dx9MDK31UhSZulQF1VxMf5b5pUk0zu8ilNbf9Ko\ndyMlliVveJf5lN/eozmjPtyC/+Z5HF03R7399PafWfLG7aXSrBzrkRB2lrBzO0iNv8KRv79Q5wC4\n5fLJjSx5w5vMSgY97ndZzYadfDG2sGXt3BFEBh0iLTGcyKBD7P39vWoPWOjqG9Gs14vs/+tDrp7d\nzvWYS2xfMoXsjKQKM9vf63tUmJ/LzqVTiQw6SHpSBLGhJ4m+dBRrl5LVBU5unkfQkdVcj7lEavwV\nLh7yQ9/IHFMr51J9Vfg96vBMhcd35zWwcqjH5ZMbib92rqSNH8dTWFh+gr5boi8dwcOnd4VlPHx6\nlQqSQkkehLt/tiZFBaFnYELrQW+w94/3Cdj3G6nxJT+Dz+5awvk9Jdn7G3byRVffiM0/TiAp8iKR\nQYfYseRVvNoMq1IA6NYx3PkKVJnH0LQXsaEnS83oSo4OJiH8PDkZ1ynIzVQfxy0pcaEEHlxBavwV\nYq/4s/H750iOCqKz7+3cAbFX/Ak5uZG0xGtEBR9m9exBqFQq9Yo1+kZmWDs30Pija2CMgYmlekWK\nO3QCtlfpBAhRCTJzQAghRI2TlJTExImTycnZR06ON4WFw8lJ2szy9zSXnPNqM4yhb65g5LRNHFw5\nne2LppCVkYixuR3OXu1o0vU5AOxr+zD0jRUcWjOTYxu+wtjcjnbD3qN5n5dvN1bm9GTNbVlpCaQn\nhj/w8SmU7uvObSaWDjw3cx8HVnzM6i8GU1SQi5m1C+7ePStchqysditjwJRf2LP8bdZ+NYKiglyc\n67dn5AebNPq6u21b18aM/nQPB1dNZ8WMXqiKizC3q41nq5Innrr6RqTEhrLhu9HkZCRjZGFHo87P\n0uZmQEZbR59DK2eQlhiOjp4BjvVaM/y9tXccjGZ/w99dy+5lb+L3aclTf3fvHvQc/22VjrP/5CVs\nXzSZ36a1x8TSgY5Pf0zOjWTNQvdY813R0sK761guHvSjXstB6nPRc/w3HN/4DYdWfYqTZ1u6j/2S\njd+NVtcrLiogJTaUvOyMMvtyqNuSZz7czKrPBwIK7Z96j5wbKaTEXVGX8ek5icTwC2z6YRyoVHi1\nGUbrQW8QsG+5ukxedgYpsaEUFVU8DV09hHI+M2Vuv2O8unqGjP50D/v/+oiN340mLzsdEysHXBt1\nxcDEsoIO7+8z2u25L1AUha0LXiIvKw272j488+FmzaU972r7Xt8jlUpFblYaW36eRFZqPIamtajb\noj/dxpQEaPQNTTmx6VvS4q+ComBX24eR0zbd8d6+Zn+V+R6VeUruaKf781+xbeHLrJjeAwNjS1oO\neJWiUtn7Nfu9kRJDzOXjDHpteYX9NOr0LPv/+ojk6GCNm9nC/NxSP1sNTWvxv1+i6TxqBsYWdpzc\n/D07f/kfekZm2Ll7q5fP1NUzZOSHm9mz/C1+/7Aj2roGeLYaTI9xX1c4lrsVFuRx+eRGRn1UcTLL\nOs36oaNnSNj5nRrLNq6ZPUQjd8iy91qjoPDeqhygJADiv2UeO5aEoqWji1ujLoyZtV89E+nWeTi0\ncjppieHoGZhQp3k/Br22/B4zY0p/ns1KPh7tgNGldgrxgBSV6v4iuo+aoiiqf8tYhRBCPNn8/f3p\n1etl0tNvZ+Y3M2vO7t2LaNWq1QO3/9qvvalj15jXB1Tt5vLf4n+/9mFA8+fp4/Ps4x5KKetOLOJE\n6E6+fG7tvQs/obIzkvjlzWY8P/vIQ0sGKERl7ftzGnnZGfR98ad7lj2w4mOyM5Lp9/KCe5Z9lM7s\nWEjoqc088+G9X8c5u2sJIcfXVT33wyPSz1uHLvW1F72/Ou/le5cWompk5oAQQohH6ot1k9h27k8U\nFBRFC2uO1+6DAAAgAElEQVRTB9p59eOlnp9hamjxSMbg7u5Ofn44EAB4AwEUFETg7u5eLe1/4bsa\nncok1LrLr3tnsWz/5+pzY6xvhpuNJ+29BjCi7WQM9R5//oKjIdtIzIihd9Pb72JvOrWU3QGruRx3\nnqy8dNa8GYK9hatGvZDYsyzc+RHBMafR0dKhc8MhvNbvK41jOnV1L0v3fsbVhIsY6pnQ12c0L/b8\nFO2b73OfvXaQ1UfnExRziqzcdJys6jCy/asMaH47d8GgFuP5/cCXBEQcxdut/GX7nmRGZjb0f2UR\nGclREhwQj52Rua166vu9tB32Lme2LyhZWeQ+Z3E8DNo6evSa8F2lyvr0mEhuVhp5OTfQNzR9yCOr\nusw8FcDHj3sc4r9JZg4IIYR4pL5YN4nkG3F8PGIZRUUFXEsKZva6F2nq3pHpT//2yMbh57eKiRMn\no6vrRkFBBL/88hOjRo1E695LSD2wwqKCMoMHv+6dxd7Av5k/cRcqVTEZ2akERB7hjwNfoa2ty4IX\n9mJpYvvQx1eRN38biI97J8Z2eU+9bfWxH8kvyEVf15D5295h9ZuXNIIDyTfiGDu/Bd2bjOCZ9v8j\nKzeDeVvfopapA7NGrQAgNC6AlxZ3Ykznd+nd1JfkjFjmbnqV9l79mdznCwD+OPAVeYU5tK3Xh1qm\n9pwI3cV3W97g4+G/0tN7pLq/n7ZPIz4tkpmj/npEZ0UIIR6NkpUYVE9O5EX8p8jMASGEEI+crrY+\nlsYlGaOtzRzp3uRptp/9U6NMVm4GP+54n8PBm8krzMHLsRlT+syhvlNz9f5vN0/l5JU9ZOVlYGPm\nyIi2U3i63ZRK1beon0/792Fs8/Fsv/ILiy9PQu9kCj9tf5+N70VgZnj7veZFuz7m2OUdLJ9yEoAD\nFzfw675ZRF0PxcLYhqGtJmncLN/9WsHT33jRr9kYEtKjOBC0kdZ1e/LZM5rHe4u2lo763FiZ2OFu\nW58OXgMY+2MLFuz8kA+eWgJAQWE+P+/8gD0X1pCZm049h6ZM6TNb42n50ZBt/Lj9PeLTImno3Iph\nrV9kxpqx6if79zqHd0vLSubU1b1M6TNHY/vIdq8CcCmm7OTZR0O2oqWlzduDflBve2fwjzz/U0ti\nUsJwsvJgb+DfuNs2ZHy3DwFwsvLglT5fMH3Vc4zv9iGGesaM6fKuRrtDW0/izLUD7A/aoBEc6Fh/\nAG/8NpC8glz072PNciGEEKImkuCAEEKIxyomJYwToTvRvutJ+tt/DMHM0Iq5YzZgamjB9rN/8vry\nfqyYGoCViR2Ld0/nWmIwc8dswNLEhrjUcNKykitdH6CgKI9jcWv54KlFWBjbYGlsw1+HvmFf4FqG\ntHpB3dbugNU81fYVoOQG+JPVoxnX9QN6eY8iOOYUX22cgrGBOcPblP8K6Opj8xnb5X2WvvIeVDGD\ney1Te3p5j2L7ub+4tVL9Tzumsf/iej54agmOFu74Hf2et34fzKo3LmJlYkdCehQfrRzF8DaTGdxq\nImHxgfyw7R2N5GT3Ood3C4g4ip6OAR52jao0/oLCfHS0NK+vno6Buk0nKw8KivLR19G/q4w++YW5\nhMSewce9U5ltZ+VlYGuumd29vlMLiooLuBh1nOYeXas0ViGEEKKmkuCAEEKIR+546A56zbSmWFVE\nfmEuCgqv9Zur3n86bB9XEwLZ/H40ejdvGCf2+ITDl7aw/dwKnu34BgnpUXg6+qhnAtiZu1SpPoBK\nVcybA+dRz6Gpum73xiPYFbBSHRw4H3GExIwYenmXLBe2+ugP+Lh3Vj/hdq5Vh6jkUP469HWFwQEf\n907qfu+Hu219svMySMtKxkDXiI3+S5g2bBFt65UsL/bOoB85E7afdScW8kKP6aw/sQhHSw+m9J0N\ngEutukQmX2bJnhnqNis6h2WJT4/E0timyu8SN/foyo/b3+PPg18zqsNUsvMzWbjrYxQUrt+IB6BN\n3Z6sOTafnef96NFkJCmZ8fy2v2Tst8rc7UjIVs6E7WfBpP0a2/V1DTHWNycuLaJK4xRCCCFqMgkO\nCCGEeOR83Dvx3tCfyc3P4Z/TvxKTEsaItpPV+0Niz5Gbn8WA2U4a9QqK8ohNCQNgaOsX+XilL5di\nztCqTg861O+vfrpcmfpQMoW/rr23Rpk+TX35+/iPJKRHYWfuwq6AVfi4d8La1AGA8OQQOnj206jj\n7dae5fu/IDsvEyN9kzKPub5j86qcotJuTjZQFIWYlDCKigtp7NpWvVtLS4vGLm0IT7wEQGRyKA2c\nW2g00dBFcyWGis5hWfIKctRP/Kuitm0DPnzqF+Zvf48le6ajraXLiLZTsDS2Ved4aFW3J1P6zOHb\nzW/w+bpJ6OkYMK7rNM5HHEFRSueBCIg4ymdrxvH6gG/VwY076esakleQW+WxCiGEEDWVBAeEEEI8\nEklJSYSHh5Obm4uBrhGOlrUBmNr/a/73ax+W7fucCd0/Akqe6FuZ2PPzpD3cnYzWWL9kTei29Xqz\n9q1Qjofu4FTYPt75YxjdGw9n2rBFlaoPoKujX+opuKejDy61PNl1fhWjOrzO/sB16qfv91LRE3WD\nB1xp4FpiEEb6Zpgb1SI5I66iQVS6zYrOYVksjGpxIzetqkMHoKf3SHp6jyQ1KwlD3ZJzsero9+rP\nAcDI9q8xsv1rXL8Rj6mhJXGp4Szc9ZFGGSiZzfHuH8OY1HOGxusfd8rIScHC2Pq+xiqEEELURA8/\nJbMQQogaz89vFW5u9enV62XWrFlPTEyMxv7x3T7kr8PfqKePezr6kJqVgIKCk5WHxp87b/jMjKzo\n3dSXD4Yt5v2hC9l+7k8KiwoqXb88fZr6sitgJSeu7CS3IJsuDYep97lbexEQeUyj/PmII9iYOT20\npQaTb8Sx+8JqujYaCpQk69PR1uVCxO1xFBcXExh1gtq2DQBws/EslSAwKMq/VNvlncOy1HPwIS0r\niYzslPs+FktjGwz0jNh9YTV6Ooa0qtOjVJlapvbo6eizK2AlduYueDk2U+87F36Id/4YysQen2jM\nNrlTTEoYBYV5eDn63Pc4hRBCiJpGggNCCCEeqqSkJCZOnExOzj7S009TWNSXc+cvkJSUpC7TrHZn\n3G0asPzmO+at6vSgiWs73v/raY6H7iQuNZzAyOMs3TuTgIijACzd8xmHgv8h+vpVwhMvcSBoA46W\nJTfNlalfkV5NR3EtMYhfdn9KB68BGq8KjOrwOufCD/Hr3llEXb/CzvN+rDr6A6M7vV0t56uouJCU\nzASu34jnWmIwG/1/4ZXFXTE3qsVLPT8DwEDPiKGtXmTBzo84dnkHEUkhzP3nVVKzkhjW+kUAhrSa\nRExKGD9tn0ZkcigHLm5g06mlwO0ZDhWdw7J4OvhgaWxb6hymZCYQGhdAZPJlVKi4lhhMaFwAGTmp\n6jJrTywkJPYsUdevsPbEQr7f8iYv956FscHtmRwrDn9HWMJFriUGs3zfF6w4/C2vD/hOPd4z1w7w\nzh9DGdrqRXo2eZqUzARSMhNKJVEMiDiCo2VtnKzqPMilEEIIIWoUea1ACCHEQxUeHo6enjs5Obfe\n7bdESzEkPDwcGxsbdblRHaYye/1LPNf5bezMXZg7ZiNLds/gq42TSctKwtLYliau7ejX7Dmg5JWA\nJbtnEJcWjp6OAY2cW/Pl6LXq9u5VvyL2Fq54u7UnIOIok3rO0Njn6ejDzGdWsHTvTP48NBdLY1vG\ndH6Xp9q8pC5z54oAJRsqP9U/MvkyQ7+qjaJoYaxvhpuNJ0NaTWJ421c0Zia80vtzFEVhzvqXuJGb\nhqeDD9+O/Ue9EoO9hSuf+65k/rZ3WXdyIQ2cWjCh+0fMXv+SOm/Avc7h3bS0tOjffAw7zvvRscFA\n9fYNJ5ewbP/nKDf/e+/PkpkW04YtVp/v4Gh/lu2dRXZ+Jm7WXrw75Gd6Nx2l0f6J0B38cfArCgrz\nqGvvzZzRf9O6bi/1/u1n/ySvIIeVR75j5ZHv1NvtLFxZ8+Yl9b93BaxmcKuJlT7nQgghhADl7ncx\nn1SKoqj+LWMVQghxW1JSEm5u9cnJ2Qd4AwEYGnYjIuKSRnBAPHyrj/3Ir3tnsf3DsrP/V0ZqVhJj\n5jfnl5cPY2/hVo2jqx5hCUG8sbw/fq9fwEjf9HEPRwghqpWiKKhUqqotGSNEJclrBUIIIR4qGxsb\nli79GUPDbpiZNcfQsBtLl/4sgYFHYN2JRQRHnyIuNZxdAav4ff8cBjQf+0BtWhrbMG3YIhLSoqpp\nlNXr+o1YPhr+iwQGhBBCiCqSmQNCCCEeiVurFbi7u0tg4BGZv+1d9gauJSMnBRszJ3o1GcnzXT9A\nR1veKhRCiH8jmTkgHiYJDgghhBBCCCHEv4AEB8TDJK8VCCGEEEIIIYQQNZwEB4QQQgghhBBCiBpO\nggNCCCGEEEIIIUQNJ8EBIYQQQgghhBCihpPggBBCCCGEEEIIUcNJcEAIIYQQQgghhKjhJDgghBBC\nCCGEEELUcBIcEEIIIYQQQgghajgJDgghhBBCCCGEEDWcBAeEEEIIIYQQQogaToIDQgghhBBCCCFE\nDSfBASGEEEIIIYQQooaT4IAQQgghhBBCCFHDSXBACCGEEEIIIYSo4SQ4IIQQQgghhBBC1HASHBBC\nCCGEEEIIIWo4CQ4IIYQQQgghhBA1nAQHhBBCCCGEEEKIGk6CA0IIIYQQQgghRA0nwQEhxH/Cqql/\ncmHL+Se2vfu1dPQCwk+GPe5h/KdtnbWRK4cvP+5hlCnqbATrp6153MMQQgghRA2g87gHIIQQ95KT\nns25DWeIOhdBdkoWBqYGWLrWomHvxrj4uD3u4T1xVCoVQTsDubz/EulxaWhpa2HtYYP3QB+cm7o+\n7uE9NDnp2az6358YmhvxzA/PVapO5NkIslKyqNOhnnrbpb1BhB0N5Xp4Mvk5+Twz7zlMrE016iVf\nS8J/5XGSryaipa2FWysP2jzXHl0DXQBCD17i4KJ9KCioUGnUHTJzBNYeNgAc//0wCZfjSY1KwcjC\niJHzNMft0syNM2v9uXL4MnU7elb5nAghhBBCVJYEB4QQT7QbSTfYPGM9ekZ6tPJti5VLLVQqFbGB\nMRz99RDP/PDfDA4UFxahpaN9X3X3zd9NzIUoWo1qi5O3M0X5RYQeDGHn3K20H9+J+j0alVlPpVKB\nChQt5UGG/tiEHgzBtYU7KRHXiT4fWalASND2C9Tr7IWi3D7mwrxCnLxdcWtZmxN/HC1VJzs1i+2z\n/6F227q0H9eJgpwCjv9+mIML99Lj9T4AeLSrV6r/k38dIzE0QR0YAFCpoF7n+qREXif2QlSZY6zX\nuT4Xt1+Q4IAQQgghHioJDgghnmhHfz2IosCQz0ego3f7R5aFo2WFN0uZ1zM5/tthYi9GA+DU2Jm2\nz3fE2MpEXSbqbARn158iJTIFHT0d7Lzs6T61N9pl3JRfOXyZo8sO0XVKD7S0tdj7w07GLJmIoqWQ\nkZDOmjdXUL9HIzpM6AzAqdUnSLqaSL9pgwCIC47F3+8YKRHX0TPSw6N9PVr7tlUHALbO2oi5oyW6\n+jqEHgrB1MaMwTOHlxrH+U1nCdx6jt5v98emrl2p/WHHr3DtxBV6vdkP1xbu6u2tfNtSVFjE8d+P\n4NLcHWNLY0IPXuLY8sN0+18v/P2Okx6bxtDZT1OYX8jpVSe5Hp5EUWExVq5WtH62Hbb17NXtLR29\ngA4TuxB7IZqocxEYmhvRfEQrjWuSeCWBo78eJC0mFQsnS1qMbM3OuVvp/9EQHBo4ApAanYK/33Hi\nL8WirauDY2Mn2o7pgKG5UbnXtjyXD1yi9ej2pLgmE7Iv+J7BgdyMHGIDo2k9up3G9sb9vAFIDksq\ns17k2QgULUV9rQHaT+zM+vdXk5GQgZmdGdq62hrHUJhfSOSZcLwHN9doq93zHQG4sOVcucEB1xbu\nHPvtkLptIYQQQoiHQXIOCCGeWHmZecQERNGgdxONwMAtekZ6ZdZTqVTs+nobuRk59P94CP0/GkJW\naja7v92hLhN9PpJd327H2duFoZ+PoP/Hg7Gv74iqWFWqvcBtARz//TB93u2Pa3N37LwcKCooIjks\nEYC4oFgMTA2JD45V14kPjlXfAGelZLLzqy1Y17Zh6Oyn6fRiN8KOXeHUqhMa/Vw9EgrAwOnD6PxK\n91LjOPHXUYJ3BTLg46FlBgZutWFub6ERGLilyUAfigqLNHIYFBYUcW7DGTpM7MLwuaMwsTalIKeA\nup08GThjGENmDaeWuw07524lLzNPo71z60/j1qo2w+Y8g0fbOhxavI+s65kAFOQWsOvrrVg4WzH0\ni6dp/Ww7Tq44hsLtJ/TZadlsnbkRKxcrhswaQb8PB1GYV8iub7aVeWwVib8US15mHs5NXajToR5R\nZyPIvZFbcZ2QeLT1tLF0sapSX8UFRWhpawaQdHRLPp8JIXFl1gk7doXC/EI8u3hVqS8Ak1omGJob\naXy+hBBCCCGqmwQHhBBPrIyEdFSosHC0qFK92MBoUqNT6PpaL6zdbbCubUO3V3ty/VqSeibBufWn\n8WhTh+YjWmPhZImVSy2aDGhaKghxevVJAv45S/+PhmDn5QCAroEutdxtiA0quVmLC46lYe/GZCbf\nICc9m8L8QpLDknBo6ARA8K6LGFsZ0358ZywcLXFp5karUW0I2hlIYX6hui9TW1Naj26PuYMFFo6W\n6u2qYhUHF+4l6kwEg2YMw8LJkvJkxKeXu9/Y0hg9Qz3SY9NubyxW0X5cJ+w87TGzN0fXQBfHRk7U\n7eiJuYMF5g4WtB3bAW0dbaLPR2i0V7eTJ3Xa18PMzozmI1ujpa1F/KWSc3L1yGVUxdBpUlcsnCxx\nbOyMz9AWGvUv7Q7Eys2alqPaYu5ggZVLLTq/1J2kq4kk3Qy8VNbl/ZfwaFcXLS0tTG3MsKlrR+jB\nkArrZCbfwMDMUOOVgspwaORE7o0czm86S3FhEXmZefivPI6CQnZadpl1QvYF49rM7b5mRAAYWRiR\nmZxxX3WFEEIIISpDXisQQjy5VKWf4ldGWmwaRpZGmNS6/QqBqa0ZRpZGpEWn4tjImesRydTrUr/C\ndi5uD6Agt4Ahs4ZjZmeusc+hoSPxwTE0HdyM+OBYGvVtQlxQzM1ZBAZoaWthU8cWgPTY1FJP+u28\nHCguLCYjIR0rl1oAWNe2oSwn/jqKlrYWgz97CgMzwyqfj4oo2gpWbrU0tuVk5HB69Unig2PISc9B\nVayisKCIzJuzAm65NW4ALS0tDEwNycnIAUqugaWLFdq6t5+w29Sx00jOl3wtmfjgWH6f8IvmmFC4\nkZCBjYdtpY4hPyefayeu0v+jIeptdTvWI3BrAE0GNC23XlFBocb4KsvS2YrOL3fn5J9HOb36BFra\nWjTs2wQDc0PKijOkRqeQGBpPn3cHVLmvW3T0dCjML7rv+kIIIYQQ9yLBASHEE8vM3gIFhbTYVNyo\nXT2NVuEpsb2XA9EBkVw9Ekqzp1pq7HNo4EjwrkDSYlIpyMnHurYN9g0cib0Yg6GZITb17NDSrnhy\nlgqVxlNrHX3dMss5NXEm7NgVos5FUK9zxQENM3tz0mJSy9yXlZpFfk4+5nfMxNDW1S715Pzggj3k\nZuTSdmxHTKxN0dLRZtvnmyguLNYoV+r4FFBpFqmQqliFS3M32oxuXyoOZGhe+SDI1SOhFOYXsnnG\nes11AYpVJFyOx87Tvsx6BqYG5GfllbnvXuq0r0ed9vXIychBV7/k/0oDt5zHtIycAJf2BGFSy/SB\nVorIy8rDwNTgvusLIYQQQtyLBAeEEE+kpKQkwsPDsalvR9DOQBr1aVLq5jk/Ow89I/1SdS0cLclO\nzSYz+YZ6CbqMhAyyU7OxdC6Zcl/LzZrYi9F4dWtQ7hhq1bahcf+mbPtiEygKzYbdnhZv5+VAUX4R\nAZvPYeflgKIoODR04vCS/RiaG2rcCJo7WRJ+4qpG2/GX4tDW0cbU9t4J5lx83HBv5cGeeTsBhXqd\ny39vvU6Heuyfv5uI09dwa6EZUAnYdBZtHW3cW3tU2F/C5XjaPd9RfQw56dlkp2Xdc5x3snC05Mqh\nyxQVFKmfziddTdDIOVCrtjXhJ65ibG2Cltb9v+V2ef8lGvZuQv0eDTW2n1p5nMv7g8sNDtRysyE3\nI5e8zFz0Te7vxtvw5kyOy/uD0dHTwamxi8b+ooIirh65TKO+3vfV/q02MhLSy51ZIoQQQghRHSTn\ngBDiiePntwo3t/r06vUyk+e9Q052Nhs/Wsu1E1dJj0sjLTaV4F2BrH9/TZn1nZo4Y+Vixf6fdpMc\nlkRSWCIHft6NtYeNOg9A06HNuXYijNOrT5IWk0pqdAqB285r5AAAsPawoe+0QQRuPc+5DafV23UN\ndKlV24arhy+r27Sta0dWShZJVxLV2wAa9GpMdmo2R5YeIC0mlcizEZxadYKGfcpOtFgWl2ZudJ/a\nm6PLDhJ6qPx36T3a1sW9tQcHF+7j0p6L3EjKIC0mFX+/4wTvvkjbsR0xtjSusC9zewuuHA4lLSaV\npKuJ7Ju/q8rT7+t0qIeiBYeW7CctJpWYC9Gc33gGuD15o2GvxuRn57Nv3k6SriRwIzGDmAvRHP7l\nAAW5BZXqJyXyOsnXEqnfoyGWzlYaf+p09OTa8avltlXL3RoDc0Pi70oimJOezfWIZNLjUlGhIjU6\nhesRyRoJGYN2BpJ8LYn0uDSCdgZy7LfDtBzVtlSSzGsnrpKfU4BnOa+wZCSkcz0imayULIqKirke\nkcz1iGSKi25PwUgMjUdbVwfbcoIcQgghhBDVQWYOCCGeKElJSUycOJmcnH3k5HiTTgDT1g/Eb/ZS\n/FceJzslC31TA6xca9FxUhd1vbtfFuj1Vj+O/X6YrZ9vAkoCBm1vLhsHJU/je77Rh7PrTnFhyzl0\nDXWxrWdPg16NS7VnU8eWvu8PZPuczQDqxHoODR1JvpqIQ8OSVQm0dbWxrWtL8rUkdb4BKEkE2Pvd\nAZxccYwNH6xBz1ifOh3q0XJk63uejzuftLs2c6Pba73YN38XiqKUu5Rjt//1ImhnIMG7gzjx51EU\nLS2sPWzo/U5/nL1dyqxzp04vdePwLwfY+NHfGFkY02x4y1KZ/5VSZ1zznOka6NL77f4c+fUgGz5Y\ng4WTJc1HtGLP9zvQvpnZ38jSmIEzhnFq5Ql2fLWFooIijGuZ4NTERR2MiAuOZeusjRrLH94pZF8w\n5g4WWDqXXnHAtZkbKpWKsGOheHVrWGq/oqXg2dmLq4dDNWZZBO++yNl1p1Bu/rdr7jb1ebk1ayPp\nagJn1/pTkFuAuaMlHV/oQp0Opa9HyL5gnL1dML4j/8WdDi/ZT3zw7eDExg/+BmDkvNHqWS9hx65Q\np0O9SgeShBBCCCHuh6K6z4Rfj5qiKKp/y1iFEPfP39+fXr1eJj399lN6M7Pm7N69iFatWj3GkYkH\nFXHqGnu+38HoheMqPY3/8v5LnFp9ghFf+5a7dOWDyMnIYd27Kxk8cwSmNqbV3v6DysnIYd07Kxk8\n68kcnxBCiEdLURRUKlXVltkRopLktQIhxBPF3d2d/PxwIODmlgAKCiJwd3d/fIMS9yX0YAjxl+K4\nkXSDyDPhnPjjCK4t3Kv0fn/0+Uha+Zaerl9dDM0M6fRiN7Ku33go7T+ozKQbtB/fWQIDQgghhHjo\nZOaAEOKJ4+e3iokTJ6Or60ZBQQRLl/6Mr+8zj3tYoooC/jlL8O6L5KRlY2hhhGszN1qOaouuQdmr\nMgghhBCiYjJzQDxMEhwQQjyRbq1W4O7ujo2NZGkXQgghhJDggHiYJDgghBBCCCGEEP8CEhwQD5Pk\nHBBCCCGEEEIIIWo4CQ4IIYQQQggNG/7+jo/f7fW4h1GuwwfW8MqEBvcsN+FZV06d3PbA/SUnRTPh\nWVfCr1144Lb+bYICDzPhWTdysp+MxK2JCeFMeNaVqIjgxz2UR+7C+f2MHenEeF8Xo8c1hvG+LsfG\n+7p89Rj79xvv67K6kmX1x/u6FI/3den/kMekN97XJWy8r8sTuazWeF+XHyp7zWTRZCGEEEKIR2jp\nwjfJvJHK1HeWVbrOlzNH4uxSn9HjPnuII7ut36CX6dV3/CPp6360aT+Yps16qP+94e/vOHVyC7O+\n2v3AbZd/fap3JndGejJvTmnNxJe/oV3HYaX2r17xBSePbeLr+certd+q8mrQlu8XnMbQ6OGumlJc\nXMQLz9Wm5DyX9SqxQuduo+g/+BWq+1qkpSbw9mttmTRlHm3aDS61f+Ufn3Hm1A6+mnekWvutqoaN\nO7J6Yxw5uUXZD6P98b4uesCbwCigHpANhADLgN+W+UUVPox+q+hFqvsD8OBeA4KX+UX539ow3tdl\nBtAbaAooy/yiSgV0xvu69AVmAI2AdGDZMr+oj+8q8xzwDlAXiAfmLfOL+qGsQYz3dekB7ADOLPOL\nan3HrtlAyHhfl3nL/KJiKjoQmTkghBBCCFFDFBYWVKqcvr4hxiYWD3k0909XVx9TMyuNbcpDv1+o\n3txXZubWNG3eg0P7V5XaV1xcxLFDa+nY5fGv1KOtrYOZuXW5+1UqFcXFxQ/cj5aWNt8vOMP3C07z\n/YIzjJ04Gy0tHb5feFa9fdSYT271+sD93cnC0o4mTbuWeS2Kigo5dng9nbo+GdciN6/8cz3e10UZ\n7+tyX/d3431d9IF9wFvAAqAd0Ar4gZIb8ub30+7NtqttmaJlflE3lvlFZVRXew9qvK+LAkwBfrlr\nlw6wCvixnHqtgE3AekoCCM8Bz4z3dfnsjjLDgF+BeZQEEKYCH473dZlQRns2wFKgVIR0mV9UHHCA\nkutYIZk5IIQQQgjxGC1d+CY3bqTSqEkntv2zgPy8HJq17MPYCZ+jq2fA0oVvEhJ8nJDgE+zZuRxQ\nmPvDUWpZOxETfZk1K74g5NIJ9HQNaNC4A77/Z++8w6Mqvgb8brLZ9N7Lpm2AAAkQeu8dhdAJIhhB\nEJymDJAAACAASURBVBRBRSxAlCgIUlQUOwZUWECB0IuU0FvovQQSFkIJpEF62e+Pu9lk0yAU8fc5\n7/P4yM49M3PuzM3unTPnnHn5Y2ztnA3arl6jMVs3R1FQkE/rdgM5fWInH0/fYKDHZxGh+KvqMXjY\nJ0T/NZfYQxsMduJ3xyxj07qfSLqTgKOzF+06DKFz9xEA/KmegSbhDO988DsAO7erWfTL+7w+dj6N\nm70IwPRP+lCnXjteCB1bZgyqWn/PzuUsXhjB91Hn2bPzT9as/BKQ8epgb0DG8Nfn0KJ1PwAy7qfw\n3VejOXl8Oza2TvTuP6HcnXqQPBD27vrLoK33pyzD0ckLkMIL/lJ/zqWLsTg5KRk87BNqB7fS13/Y\nfJSmdbsw5s1+lbtJGpyclfry40e3kp5+j1ZtBwBw+9ZVlv7xKVcuHyc3JxM3d3/6DHiP4Hrt9HXe\neaMx7ToO4dbNqxw9vBFLKzsGDYmgVlALFv7yAaeO78DOwY2Xw6dRK6gFIIUMzJ4+mHHvLWTFspnc\nSozDyzuQV0bMxNu3toHM/F/OYG5hza4dS1m2+DNeG/MVf6k/59bNq3w2aytu7v7s3K5my4afSbpz\nDSdnJe07DaVjFTxQShohijwVbGwcDWTS05IASLqTwNI/Irl86QjOzkpeeuVTatZurpe7rjnP8iXT\nuXT+EAqFObWCWxH2ckSFho7W7cKY/9Uoku/dxMHRXV9+LHYLGRmptGzTH4CbiXEsX/wZV+JOSHPh\noaLfwPepXae1vs740Q3o1PVVbly/yLHYzVha2RM2NILAms1Y+MsHnD4Rg72DO0OHTyewVjNAChn4\ncuZQ3p64iD/Vn3Pr5hWUPrV4ZcRMlD419TLDBkphBVFqTWZ4mHIU8BkwHGl3uBoQCFwJD1OOBN4G\nfIGrwPwotWZ+JcP/PpIxoF6UWnO2RHl8eJjyT6Dkzrexzk19OJCLtOP9UdHF8DDlTSSjQiAQCqwG\nhoaHKUOAuUATJK+EVcDbUWrNA109NdL69CCSB4MpsAJ4I0qtySshYxyl1gzQfZbpdB8OKIHbwIIo\ntaZcN6vwMKU3MBvoBBQCe4BxUWpNvO66L/AN0AJQAPHA5Ci1JrqCcWsBeAEG8UtRas1kXXsvVVBv\nEHA4Sq2Zqft8JTxM+RHwa3iYclqUWpODZDD4M0qt+VUnEx8eppwFfIBkNCjJQmA+4AB0oCxrkDwQ\nPq5AH0B4DggEAoFAIBA8dy6eP8SN6xd5b9JSRo/7nqOxm9mySXr3Gzx0KqpqDWjZdoBuF/UIDo4e\npKXeYUZkf7y8A/l42nrem6wmJyeTeXOGG7R94dwBrmvO8+6HfzBxkppmLfuQEH+GWzev6GXu3E7g\nyuWjNG/VR1ciM9iJ37ltCSuXz6LPwAlMnxPDoJemsHHt92zfsgiAwFpNuXzxiH4H+cK5A1hbO3L+\n7H4AcnOzuHrlJIG1ihdvJal6fRnIJP2aNHuRLj1G4u6h0o9PkUEBYM2qr6nfqAuRM7fQuNmL/Prj\nBJLv3SxXj24vjKJR0xeoFdxS31ZA9Yb666uWz6JTtxF8OnMLfqo6/PDNm+TkZAE88nyUJLhuW2zt\nXNgdYxhCvTtmGTWDWuDo5AlAdlYG9UI6MnHyUqbO2ERIwy58M/c17tyON6i3ZeMCqtdoxNQZm2jQ\nuBs/fzeOn+aPI6RhZ6bO2ERAtfr8/N24Mh4kfy6ZxqAhEXw8fQMOjh58NSucvLycEhKGXhm5OVls\nWPMd4SNnMW32Nuwd3Nn+92+sXjGXvgMnMn1ODAMGf8Ta6G+I2bZYX+/Hb9/i/bdb8TRYsWwWXXuM\n5NOZW/D2DeKHb94kLzcbgJTkW8yMHICvXzAfT9/IhElLyMq6zzdzX6uwvbr1O2Bt7cCenYbeA7tj\nllE7uDX2DpLBICc7g5CGXXh/yjKmzthE3ZCOzJsznLtJGoN6mzf8TGCtZkydsZmQhp35ef54fv5u\nPA0bd2PqjE34qery0/xxFBQYeuovXzKdwcOm8vH0DdjaOfP17FcN5qucw9usgQnAq0i7yzfDw5Rj\ngclIi+ZApMVkRHk7ziUYDGwoZRgAIEqt0UapNRklil4FUoHGSJ4GE8PDlL1KVXsPOArUAz4JD1Na\nA5uQXOMbAP2A9kheCiXpCPgAbYGXkBbRYyrRe65Oh0+Amjr5cv/Aw8OUVkAMcA9pUd8cSAH+LuHd\n8LPu/y2BIKSxrcxToSVSSEF2JTLlYQqUrpMNWCJ5ElQmowoPU7oUFYSHKd8BzKLUmlmV9HdIV8+9\nEhlhHBAIBAKBQCB43lhYWDNs+Oe4e6ioHdyKRk16cO70HkDaQZXLTTBVmGNj44iNrRMymYztf/+O\nt09t+g36ADd3f7yUgYx4fS5X445zNe6Evm2FiRnDX5+Dp1d1PJU18PCshrdvbfbvWaWXObA3Gjd3\nFb7+dcrVb230PAYMnkSDRt1wcvaibv0OdOs5hm1bfgOgeo3G5OZmc/WK1O+Fcwfo+sIo/eL+0oVY\n5MZy/FR1y23/SeqbKMwwNbXEyMhYPz4mJqb6681b9aVpi1BcXH3oM+A9jIyNuXC+/Dh+UzMLFAoz\nTOQKfVvGxsWOtp27v0bdkPa4uPrSd+D7ZDxI4VrCGYBHno+SGBkZ0bJNf/bu+lNflpaaxKnjMbRp\nP1hf5uMXRJsOg/H0qo6Lqy89+4zDU1mD2IOGyRbrhrSnTYfBuLj60qvv2+Tl5eDuGUCzFr1xcfXl\nxd5vkZZ6h5s3LhvU69XvHWoFtcDTqzrDX59LdtYDDu1fW67OILnaDx0+HVW1+ri6+WFqas666G8Y\nOGQK9Rt1xcnZi3oNOtHthVF6AxKAvYMbrm5+FbZbFbq9OIrgeu10czGR++l30VyTkhRu/3sRfqq6\n9BnwHm7ufii9a/LqqNnEXTrCtfgz5bZnZGRM89b92bOzeC5Skm9x+uRO2rQP05f5+tehdbtBeHhW\nw8XVl9B+b+PmruJIqcSX9Rp0onW7Qbi4+tCrz3hyc7PwVAbSpHkvXFx9eSF0LKkptw2MdAC9+79L\nYK1meHpVZ8ToL8l4kMLhg+sqGwoTYGSUWnMwSq25FKXWZAGTkHbk10SpNQlRas0apEX0G5W0owIe\nNcvj0Si1ZnqUWhMXpdYsAfZRdrd6S5Ra83WUWnM1Sq25AryCZGUaFqXWnItSa2KQFv2Dw8OUniXq\n3Y1Sa8ZGqTUXo9SaTUB0OW0DEB6mtNe18U6UWrNY19f+KLXm5/LkgaHAgyi1ZnSUWnM2Sq25ALwG\nuAJddDLewC7d9fgotWZTlFqzvZKx8AESK7leEZuB1uFhyv7hYUqj8DClEmneANxLyPQPD1O21d1v\nLeCtkjLhYcoGSAaMIQ/pLxFp/H0rExJhBQKBQCAQCATPGQ/PashkxbuzdvauXIk7XmmdhKunuHDu\nAKPDA0tdkXHnToJ+Ie2prGGwwAVo1rI32//+jd793wXg4L7VFbra309PJvleIot++YDfFnyoLy8o\nyNfrbGpmga9/MOfP7sfa2p6srAe07zyM6BVzSUtN4sK5A6iq1S+jRxFPWr8yvLyLx8fIyBhra0fu\np92rcjul27J3cAPgftpd4NHnozSt2g5kXfS3nD65k6A6bdiz608sLG0IadBZL5OTncmqv+Zw6vgO\nUlPvUFiQT15eLv4BIYb6KYv7trCwwViuwNOrhr7MxlYKb0hPv2ugnyqgOJzc3NwKD6/qJN64VOE4\nyOUmBn2lpd4hNeU2UT++x8KfJurLCwryMZYXh5sPGPwRT4uS/dvZuwKQni7Na/yVU5w/u7/8ubid\noA+ZKE3rdgPZuPY7zp7eS62gFuzZuRwrawfqNSg+uSMr6wHRf83h9ImdBnNx767h+rCkflbW9hgZ\nGeOlLJ4LW1snQMv99JLPogxVteK5sLCwwd0zgMTrFc8FkBWl1pwv+hAepvQCXIBF4WHKhSXk5EBW\nJe1UJWnHyVKfE3V9luRIqc+BwLEotSa3RNkeXb81gaJEeafLabt6BXoEId1XZYv3ktQHaoaHKUsf\nvWGOZBwB+Ar4WucJsQ1YGaXWlG/dK65bVa8BotSateFhyklIngpLkOZmOpI3RlFiiflIi/kNOs+G\nFF1ZBFAYHqY0B9TAWF1egcoomnvzyoSEcUAgEAgEAoHgOVN20StD+5Akb4XaQurW78CgIVPQlvI1\ntrUtjnE3NS37Lti0eS/+XPI5cZePYWws52ZiXIXGAa1W0mPYiBkGC5fS1KjZjHNn9mJt7UD1wMaY\nmprjrwrh3Jm9nD+7nzol4uOfRf2KMDY2zIUmk8n091T1tsq+Ohfqxv5R56M0zi7e1KzdnN0xyyTj\nwM7lNG/ZB3mJRfWS3z7h/Ln9DHxpMi6uPigU5vz47VgKSoUHlFyIg7TqMpbLS3yW1n+Pe/9FKBRm\nBp+LwkHCR36Bn6qeoQ6yZ5MosuS8FvVR9Dej1RZSr0EnBgz+qOxc2JVewxbj6uZH9cAm7I5ZpjcO\ntGjdDyMjY73MkkURXL54hAGDJ+Hi6oOJwozv540hPz/XoC15Oc+KwfOj0/kpJHMsfXJBkWf4K0hu\n/SWprLPLSIv0R6F0ZlMtYFyqLINHp+Qkldf20/J2NwIOIHkQlH4w7wJEqTXfh4cp1wLdkUIcDoWH\nKadEqTUVHQV4l4fsxleErs0vwsOUbkAyUAuYBlzRXS8E3g0PU04A3IA7wItIYxKP5OWgAtS63AtF\n9ygLD1PmAh2i1JrduvKiDK5JlekkjAMCgUAgEAgE/wBJSUnEx8eTnV3lTSaM5SYUFhYYlPn4BhF7\ncD2OTp4Gi5dHwdbOhcDazdi/eyVyEwUB1RsYJMQriY2tE3b2rty+FV+hAQGkvAHbNkdhaWlLYM2m\ngHQM3olj24m/cpL+D9k1fpL6crnJU8mYD2AsVzxWW08yH63bDWLBD+9yNHYzt29epfU7YQbXL1+M\npWXr/tRvKHk+5+RkkXTnGkrvR13LVYaWuMtHcXDsAUg744nXL9Ku48O8lIuxd3DDxtaZO7cTaNK8\ndOj5s6Byg4OPXzDHj/yNo5MXRkZVW1e2bhfGogUfcOTwRpLuaGjdbpDB9UsXYmnVbhAhDSXPjpzs\nTJLuXMPPv3zPkKqhJe7SUeo36gpAZmY6N29cplO3ivNWlIMGacGqilJr/qpCvSXA5PAwZe0otcYg\n9kJ3AoJ5qbwDVeUckou8qS7ZHkArJIPF+YqrVcppoAAp7OC3R5A/CvQA7lR2L1FqzXXgJ+Cn8DBl\nBFKW/4qMA8eAl6uidDn93QIID1MOBuJKj3+UWqNFl0dBJxMTpdbcDw9TXgGCSzX3NlLCx4FIBoQi\ngpAMSZWGjgjjgEAgEAgEAsEzRq1exvDhY1AofKlXO5HGjcqP7a8IJ2clV+KOczfpOmZmllhZ29Oh\n8zB27VjKd1+PpvuLo7G2ceTO7QQOH1xP2JAITM3KHKttQPOWfVj6x6fI5Sa8GPpWpbKh/d5l8aII\nLCysqRPSnoL8PBLiT5OSfIsevaQw5uo1GpOfn8fRw5vp9uJoQFrwr4v+BhMTBf6ldpRL8yT1nZy9\nuHf3OglXT+Po5ImZuSVyuaLS/ipsy8mL0ydiuHXzClZW9vqs+Q/jSeajfqOu/B41mV9/nIB/QD08\nPKsZXHd19+PI4U3UDemATGZE9Iq5j3ws5aOwZsVXWFraYmvrTPRfczEztzRI6vgoRwf26vs2y/74\nFDNzK4LrtqUgP4/4q6dIS02ie09pPpcvnobm2jne/fCPJ9S4cn06dnmF3THL+GHeG3R9cRTW1g7c\nuRXPoQPrGBL+qUFOitI0atKdxYsiiPppIgHVG+Dm7m9w3c3dnyOHNlCnbju0aFn15+ynZpgCiP5r\nLuYWNljbOLBq+WwsLG1p1KTHI9ePUmu04WHKqcCM8DBlBlLcuilSEkCnKLVmTgVVv0CKu4/RLYj3\nIiXia4QU0z4WKand47IIKUniwvAw5WdIYQjfAkui1JobldasgCi1JiU8TPkdMCc8TKnV6ewM1Kkg\n78AiYDwQrRuj60g5A3oDc6LUGk14mPIbpMz+l5B22zsB5SeqkNgG2IeHKatHqTUXiwp1pyLY69qX\nhYcpi6xHF6PUmqzwMKUcaUy36MoHIuUT6FmiDTckT4FdSIkKXwO6ISVSRHeCg0ECyfAw5V0gO0qt\nKW0EaAXsiFJr8qkEkZBQIBAIBAKB4BmSlJTE8OFjyMraQVraEfLzu3L8+EmSkir17jSga49RyOUK\nJr3XnrdG1ePe3RvY2bsyaepKjGTGzJ05lMkTO7J4YQQmJqbITR6+MG7QqBu5OVk8uJ9SaiFYltbt\nBvHqqNns37OSjz/owueR/di5fQnOLt56maK8AaZmFvj4BgHo8gQYo6re4KH5Ap6kfsMm3alTrz2z\npg3irVH1OLhvje5K2R3mh7m5t2k/GHePAKZO6sFbo+px+WJshW2VLHuS+TAxMaVZyz5kZqTTul1Y\nmeuDh07F0tKW6Z/04evZ4dSo2ZSA6g0eel/l32vpMhn9wj5E/dtUpk7qwb17Nxj/3sJSC+iHhwa0\n6ziEYa/NYO+uv/j4gy7MiOzP7h1LcXYtfkZSU++Uyer/eFR+X/YO7nz0ySq02kLmznhZmotFH2Nq\nalEmzKQ0JgozmjbvRWZGukFSyCJeeiUSU1NLpn0cyrzZw6kd3Br/Uvkkyh+tR5+LxQunEDnpBVJT\n7zD+vYVVNnTpjiwcjXSqwEmkDP3h6NzVK6iTDbQD5gCvA/uBw8A4pBMFinIIPNxSVI5MlFpzH8n4\n4IpkZFiOtLAe/QjtVcbbSHkCpiItlJfp+iiji06Hlkh5DFbo5BcgxeGn6cRMgO901zYgHQM5oqLO\ndbv+aymbEHAmkqfCp0hHIh7V/Ve0068FeiHlXTiENPbdotSaLaXaGQ7EIs2hH9AqSq05VZE+lTAI\nyRuiUmSl43D+rchkMu3/iq4CwX+JTxbXY0Cr2dTy7vjcdDh+ZQ3rD3/OpIH7n5sOJUl9kMhXq7sz\nspsaD4fyXT6/jO5GkxphNK859Kn1+6zHYdX+KThYKWkTPPKZtP8kXLyxm20nvmF09+UPFxYI/mEO\nHz5Mp06vk5ZWnJ/LxqY+W7f+SKNGjZ6jZoL/OmdP72H29JeY/8vpR/aQEDwbTp2I4cuZw/g+6ny5\neUKKkHJmaJ9NIgfBYxEepgwB1gEButMi/lWEhyn7AJOj1JqKk8boEGEFAoGgXFbtn8KJK8XHGJmb\n2uHlFEyX+u/iZOOrL5/QdxvmCpsn7isrJ43Bbec9dhuyKiXZ/Qd4yM7UqG5qTOSVJozVE387loVb\nR0htlmcklckIbRop/fMZjcPtlEtcuL6Td3pv0ped02wj9tJf3Ew+R2ZOKq90XICvq+FOVvL962w5\nNodrd46RX5hHNY8WdGv4PlZmjnqZxORzbD32FTeSz2Akk1NT2Z6uDSagkBe74N64d5qtx78hMVny\n7PNwqEXHem/h6SjtLlb3bMWOk99z8up66vg9uvulQPBP4OvrS25uPNIGXh3gJHl5Cfj6+j5XvQQC\ngUDw5ESpNcd0Jw/48ujHQf6TKJA8EB6KCCsQCAQV4u/elPf6bmdC320Mbf8D+QU5LN31toGMlZkj\nxkaVu+j9J3mIp5OFqR0mxhXHPJZE6VyPCX23MaHPVib03UY9VS+UznX1czOhz1aCfLo8vKEn4OBF\nNbW8Oxos2HPzs1A616Nrg/fKNYbk5mfx+/bXAXil0wJGdF5EfkEuS2KKY5vvZyXx27ZROFh7M7LL\nYl5u9x1JaXGs2h9Rop1M/tjxBjYWLrzW5Q9e6/IHVmZO/L59NLn5xUma6/n35MCFxc/i9gWCJ8LZ\n2ZkFC77D3LwdNjb1MTdvx4IF3+HsXHEGe8HjM3JeG1bvX/DM2k/PTKF3ZABnEp4k/PqfY+nOebz1\nQ7fn0nfvyAD2n9v8XPoWPFtCI1XbQyNVj5618h8kNFI1JjRStebhkk+PKLVmYTlx/v8KotSapVFq\nzbFHkRWeAwKBoELkRgoszaSTT6zMHGkWOIQlO8eRX5CL3FiKfysdVnD97knWHZ5OUtoVnG396VDn\nTRbHvFnurvKjkp33gC1H53L++g7y83Nwd6xJl5B38XCsZSB34fpONh+dQ1rmLZROdejV9BPsrbz0\n1w9f+pN9534jLeMmtpbutKwVToOAPgD8fexrbqVc4OX23wFw5PJK1h6MpF/LmfqF94Itr1DNoyWt\ng0aQlnmbDYc/J+HOUfILcrCz9KBtndfLXaRrtVrWH/6cyzf3MrT9DzhYK6sUVmBsJDfYaTcxNsPY\nyEQ/N6W5cusgG2O/IDXjBp6OQYQ2jcTOyqPEOMUQc+pH7qTFYW3uTLBPN9rWGVWhkadQW8iZa1vo\n0+wzg/K6fi8AkJmTWq4xRJN0nNSMREZ1X4qZiRUAvZt/xow/W3Hl1kH83Zpw4cYujGRGvNB4kr7e\nC40n8/36/iTfv46DtRd30+LJyk2nXfBo/X20r/sGp+I3cDc9QR+6EejVlo2xM/T1BILnRXpmMkti\nvuLo5Z2k3L+DpZkNPi412LT3d8zznfH19WWSuh8W+x/Qq1mVspA/EjfuXeXN+Z34PHwZgcri792I\n31/mdPwBFr57CBsLe3358K9a0LFef8Lajn/qulSFpTvnsWznvCK3aX25nZUzUe88erjU7BHRmCkq\nT8b4pDyN4/l6RwYwsf98mtV8POPu9hMr+Gb1+2XGqwiZTManQyWDaUVeZbWCWvLrkoSn0r+dlTO1\nvBsytOP7uNr9s9/B81ZP5H5WKpMGPTSk+plQNBbujr5898ZWg2tHL+/k0yXDMVNYov7gRIVtBNdt\n+9hz8U8TGqnqAXgBi0uUvQaEASGALeAbHRF3rVS9+sAMpCSH+cBK4J3oiLiMEjIdgEik2PwHSKcQ\nfBQdEVdYQmYA8CFQHel4v/nREXGzS3T1CzApNFLVIjoibu/Tuu//AsI4IBAIHomcvAxOxW/C1a6a\n3jBQmtz8TJbEvIXKvTl9m3/O/aw7bIyd+VAX+4exeMcbmCtsGdJ2PmYKG45fXcOibSMZ++JqrMyl\nRXN+YQ47T/9I7+afYWJsyobYmSzb9S6vd18GSC7wG2Nn0LXB+6jcm3I5cS/rD0/D2tyJ6p6t8XNt\nyOFLyynUFmIkMyL+diwWZvbE344lyKcLefnZJN47Q+cQyXNi3aHPKCjMI7zTAkzllty9H1+u7gWF\n+azcN4mktDhGdP5Nr++zIr8whz1nfqV3s08xNjZh1b7JrD30md7ocTlxLyv2TaJ7ww/wcalPWsZN\n1urupXP9t8tt83bKRXLyMvBwrF01XQpyARlyo+LnRW6kQIYR15KO4e/WhIKCXIyMDH+K5DqPimtJ\nx3Cw9sLRxhdLU3uOxq2iddBIQMuRyyuws/LAxValr2dr6YalmSMJd2KFcUDwXJmxfAx5+TmM7TkT\nN3tv0jLucSbhEEYKLY1Cnm6OgfyCPOSlEqx5Ovphb+3CqfiDeuNAfkEeF64fw9HGnTMJh/QL0pvJ\n8SSn36aOX/OnqsPj4umkYtqwJWhL5DMzklXtWMCSho9nxb8hD1ar2i9QP6CN/vOXq97FxtyOEV0j\n9ONnbW7HqfgDz0wHU4UFP47dgVar5frdOL5bP4nPl43iy5HrnooB5X8JE7kpGdnpnEk4TG2f4r/z\nrcf+xNnOk/uZqc9Ru6fOW8DC6Ii4kn8IFkgnI0QDX5auEBqpcgf+RkoY+AZgA3wNLAT662TqAuuB\n6UjHA3oCPyJ5u0/UyXRDMkq8qeuvJvBLaKQqMzoi7juA6Ii43NBI1RKkZIrCOFAFhHFAIBBUyKXE\nvUxb1gyAvPwsbC3deandtxXKn7i6Hi1aejX9BLmxAmdbP1oHvcaKfZWfbV0ZV24d5HbKJSb2i9Eb\nJdrXGcOF6zGcuLqOFrWGAdLudreGH6B0ko4H69N8Gl+v7qHfod537jfq+vWkcfUBADjW8CYx+Sx7\nzkRR3bM13i4h5Bdkk3jvNF5OdUi4c4TmNYdxLC4agGtJxzEykusXyGkZt6jl3RFXO+m4qZI78wDI\nZOTmScaSnLwHvNopCjPFs0/2VKgtpEfjSThaS9mhm9ccxuqDn+iv7zqzgBa1XqGev5SZ3N7Kk071\nxrFi30cVGgfSMm4iQ4a1edVcoL2c6qAwMWfz0Tl0ChkPaPn72NdoKeR+1l0A/Nwas/noHHaf+ZXm\nNV8mNy+LrcfngUzG/Swpk7upiQWvdPwF9c7x7D4juQrbWXnycvsfyhiqrC2cSclIrJKeAsHTJCP7\nPueuxTL15d8I9m0KgLOtBwEexUdRT140mKTUGyzaOoOFf3+OTCZj5ZRL3M9K5aeNn3D22mHuZ6bi\nZq+kV7MRdKjXz6Cul3MAZibm7DixEhc7JbNGrCyjR7BvU07HH6B/qzEAXLh+HBsLe9rWCeVU/H69\nceDk1f0oTMyo4RUCwOXEk/yxfS5Xbp0hvyAXH5dAXun0gf46SDver3X7hJNX93E8bjddG77Eyx0m\n8OuWaew/t5n7mSnYWTnROqgXL3eYUKXxMzYyxtayYiPqyHltaF+3LzeTEzh44W/MFRb0ajaC0GYj\nDGR6NBqq98rYfGQJqw/8yt20RMwUlgS4BzF58AKMZEZotVqW7/6Wv48uIy3jHh6OfrzU7h0a1yhO\nsnvpxkl+2DAFTdIlvJwDGNy27HelJukSi7bO5EzCYRQmptTxa87wzpOxs3Kq0v1XBRO5KXby4vA0\nE2MFChOzCsdvz5l1/LF9DmkZydTxa8abPWdgbW6nv77t+F9E7/+F2ynXcLb1pEuDwfRsGl6pDjJk\n+v7srJwY2Potvo5+l5vJ8Xg4+gFwPyuFL/4ay9FLMdhaOTG47XjaBPfSt/H7tlkcOL+FpLRE7Kyc\naFGrO4PbvY2J7vt96c557Du3kQGt3ihX/6U757HjxEpkMhm9IwP0HhO1fRoTf/sCUVumcU5zdN86\n1QAAIABJREFUBFMTMxpV78CIrlOwMJV+j+etnkh6Zgq1vBuy9mAUOXnZdGs4hCHt32XpznlsOrIY\nI5kRLzYJp0+LUZWOhZGRMW2DQ9l6fLneOJCemULspe30bj6KNQd+NZA/rznC79vncDnxJFZmtjSq\n0YFhHSZibip52k1eNBilczUszazZcnQZMpmMdnV6G7QRGql6CWkBHAhkATuB8dERcYklZGogHVPY\nBjBGSn4yMjoi7ozuejjSUYX+QALwQ3RE3FcV3WdopMoJ6Kiroyc6Iu5r3fWK3ERfAAqiI+LGlGjr\ndeBkaKTKPzoi7gowADgTHREXqRO5EhqpmggsD41UTdV5GAwB1kRHxP2ok4kPjVR9DryPdMpAEWuA\nLaGRKrPoiLjsiu5HYIgwDggEggrxdW1AzyYRaLWQnZvOoUvL+G3bKEZ2XYyNhWsZ+Xvp8bjYBhgs\n2Dwdgx8af18ZN5PPk1uQxcy/2hiUFxTmkvyg+DgkGUZ4OhTvbNtZumNt7kxS2hX83ZqQlHaV+irD\nH1Vv5xAu3tgFgEJugbtDLa7ejsXC1J6cvAwaVx9IzMnveZB1j/g7sSid6mCs2+VuGjiYdYc+43Li\nXvzcGlNT2cHwZAKtlpX7PsLa3JlXOv6CidzsscegKsiNFHrDAEiL5YLCPLJy0zFX2HAz+SyJ986w\n50zxS4pWW0hBYR4Psu6V69mQV5BdZnf/UbA0s2dAy9msOzyNw5eWYyQzIsinG+72gch0KW9cbFX0\nbvYpm4/OYfuJbzEyktOkRhiWpg7IZEa6/nOIPvAxSue69Gs5k8LCfPad+w11zDhdYsfisTUxNiU/\nP6fKugoETwszhQVmCksOXdxGTWUDTORlc4t8MOB7xv/Yg04hA+nSsPjYurz8HFTuQfRtMRpzU0tO\nXtnLD+un4GLrSbBfM73crlOr6Vx/ENPDl1f4/Rrk25RfNkXqd/VPxx+gtk9jgnya8MvmT/VypxMO\nUsMrRL/zn5WTQbu6vXmt28cAbDj8G5+pR/D9m9uxMrfV11u+6xuGtJ9AeOePkCFj3cFFHLqwlff6\nzsPZzpN76be4ca/41DR1zNcs3/UNqyIuP+bIFrPmwK/0bfE6g9qM5VT8AX7aOBU3e2+aBnYuI3s5\n8SQ/bZzK+NDZBCobkJF9n1Px+/TX1x6MYvX+BYx54TNU7kHEnIxmxvIxzB25Bl/XQLJzM/ls6QiC\nfZsyPnQO99Jv8cvmTw12xVMeJDFp0WA6hQwkvPMk8gtyWbx9LtOXjeKL4Sue+H6fBrdTr7PnzAY+\nGvgj2bmZzFrxFou3z+H1HtKzsOXoUpbunMfIbh/j7xbEtTsXmb/uI+TGJnRv9Ohh5Qrd815QWHyU\n+vJd3zK040SGdniPv48u55s1H1DbpzFONu6A9DfzVq8vcLB2RZN0ie/XT0EhNzUIc7mTeqNC/UOb\njeD63cs8yErn7d5z0KLF2tyOnLwsIpeEU92zHnNeiyY9M5X56z7k2zUfMrF/8UbH2WuHcLJ157Nh\naq7eOsPclW9z9dYZ/N1rMyN8OSev7uOH9RHU82+Jv3vFHnQyZHQMGcB7C/owqttUzBQWxJyMJlDZ\nAFd7Q2+2+NsX+GRxOIPbvs3YnjO4n5nKgs2f8s2aDwx023V6DS80foWZr/7J1VvnmLNyPB61LUs2\nZQJEAOcBJ6Tj85YAbUG/W78H2A10QDqurzGSkaAoFOATpF34o0AQ8HNopCq3aBe+HFoC2cDpCgej\nfEyBvFJlRYv2lkjHLJqWKCspYwo0AHZVIuMVGqnyLhHKEIs0Ps2AHVXU9T+LMA4IBIIKMTE2M4jZ\n7+nwMZ8vb0Hs5RW0rzOmkppPD622ECszR4Z3XljmHdjUxNKw4LFcGIvr+Lo05OrtQ1iY2uHtEoJC\nbo6XUzBXbx8i/nYs1Txa6mXrq3oT4N6CS4l7uHLrAAs2D6VV0HDaBr+ul6nu2ZoTV9dyLekYKvdm\n/BOUdr8tijMtcoHVarW0CR5Fbe+yL9EWZuW74lqY2lNQmEdeQc4jJ1EsQuXelHE915KZk4aRkTFm\nJlbMWtGBIF9PvUywbzeCfbuRkZ2sP8Fh/7nfcdA9eyevriflwXVe6/K7vk7fFtOZ8Wcrzmm2U8ev\nu748Kye9wvsQCP4JjI2MGRf6BfPXTmLLETV+brWoqWxA81rdqe4pnYVuZW4r/T0oLLCzLN5ZdrB2\nNdgB71R/ICev7mPXmbUGxgEXOyWvdPqwUj2CfZuSm5fNhevHqe3TiFPx+2lbpzeByvrcTE4gNeMu\ndpZOnI4/SPdGLxfX8zP8rhrRJYJ9Zzdx5PJO2gT31Je3DHqBjiH99Z+T0m7g4eBHTe+GADjZuBt4\nG9haOuDlHPDQ8dMkXWbQjDoGZY2rd+CdPsVeytU969G3pfRd6+7gy6UbJ1hz4NdyjQN3029iprCg\nUfUOmCkscLYFX9ca+uur9y+gd/PXaFlbyqES1nY8ZxIOEb3/Z8aHzmHnqdUUFOQztucXmJqYoXQO\noH+rMXwdXbxpuil2MX6uNQ28JN7q9QUvz2rA5cSTBHgY3s/zoLCwgHGhszBXSL+bnesPYseJYsPF\nn7vnM6zj+zQNlDxKXOw86dNiJBtj/3hk48Dd9JtE7/sZRxt3vdcAQLs6vWkdJD07g9u9zbpDCzmT\ncFj/PPVv9YZe1tnWg34tX2f1/gUGxoHK9DdTWKCQm2Eizzbwmth2/C9y8rJ4u/ccTE2k35YxPaYx\n5beXuJVyDTd7yZBuaWbDqG5TkclkeDr6Eb3/F1Iy7jKkvTSf7g6+rNj7I6fiD1RqHABQOgfg7VyN\n3WfW0SlkAFuPL6dvi9EUFBquiVfv/5lWtXvoPTPc7L0Z2X0q7/7Uk/TMZGwspJxCSqcAwtqO0+ux\n+ehS4vw36NuJjohbWKLZ+NBI1RvA2dBIlYfOe+BNpLj9/tERcQU6uZIWusnAxOiIuFW6zwmhkaqZ\nSG7/FRkHfIA7pUIKHoXtwJzQSNX7wFzACvgc0ALuOpnNwHidR8RSwA2YortWUubL0EhVJ2ArUA14\np4TMNYDoiLis0EhVGtIJAoJHRBgHBAKBAUlJScTHx5OdnUN5+YtkMiPy8sv3znKy8ePE1XUGCQtv\n3Dv1RDkH3B1qkpGdDMhwsPasUE5LITfundaHFaRm3OR+VhLOtv4AONv6cS3pOCGqUH2da0nH9NcB\n/FwbcuiiGnOFDb6u0guuj0tDLt7YRWLyWZ1rfDE2Fi40COhDg4A+7DkTxcELS4qNAzIZ9QP64OYQ\niHrneMLafI3Kveljj8PTwt0hkLvpV6sUk+9mXwO0WpLSrhh6R1QBC1Npx/HKrYNk5qQQ6Nm2jExR\ngsWjcauQG5virxuv/IKccpJpyQAZWvT5icgvkLxJHldHgeBp0TSwCw2qteNswmEuXD/GsbhdrN6/\ngCHtJ+gXteVRqC1kxZ7v2Xt2A/fSb5NXkEtBQR5Bvk0M5FTuQQ/Vwc3eGydbD07HH6C6Zx0u3jjO\n2J4zMTUxJ8A9iNPxB/F1DST1QZI+/AEgLeMei3fM5XTCQVIf3KVQW0hefg530wzDdUrr0L5uXz7+\nYxhjvu1APVUr6ge0oUFAW/0Oe/dGLxsYISrC3cGHiMG/GuQcKJ1csKTRQfpcnwPn/y63vbr+LXG2\n9WTkvDaEqFpRz78lTWt2wVxhSVbOA5Lv3ybQy/Do75reDTl6eScAN+5ewcc1EFOTYg+lGl4hBjkH\n4m6e5kzCoTJGDZlMxq2Ua49lHEhKS2Ts912ldpDRr+XoSp+dh+Fi56lfWAM4WLuQmnEPkBJo3k27\nyffrJvP9+il6mcLCfMp9EShBdm4GYTPqotUWkpufjco9iPf7z9d72QH4lDDGGBsZY2PhQJqub4B9\nZzey9uBCbqUkkJWbSWFhAVptoUE/lelfEdfvXsHXJVBvGAAIVNZHJjNCk3RZbxxQOgUYeILYWTph\naWZr0JadpZOBzpXRMaQ/W48tx8elBsnpt2leswu7z6wzkIm7eZpbKdfYfWZ9caFWKz0zydf0xgFf\n10CDeg7WLphaFm8C6BL8RQD1AAekCdMC3kCirnxPCcNAybpOgBL4MTRS9UOJS3JdGxVhTtmd+4cS\nHRF3NjRSNQzJMDANyYtgHlJCwUKdzN+hkaoJwLdIuQiygU+BViVkfg6NVPkj5TZQIHlDfI3kAWH4\n4EihFo92brQAEMYBgUBQArV6GcOHj0Gh8KX94DSatgjiQZb0Y5iVm87BC2ry8rOo4dmm3PrBvt3Z\nfuJbVh/4hNZBI0jPvKOPEX9YYqKcvAxupVwwKDMzsUbl3hSlU13UO8fTKWQ8Tja+PMi6y+Wb+1C5\nNcXbRXpJNJIZsSn2C7o2nIjcSMGmo7NwsQvA3016qW5R8xWW73kPd4eaqNybcSlxD6fiNzKodfFu\nlLdLCAWFeZzTbKdFLcma7+fakN1nfsbYSIGnY/HL8MbYL6jm0QJHGx+ycx9w+eZenO2Kk+MVuTk0\nDOgLWi1Ld73NoNZfPh8DQYmX2DbBo1gS8xa2lu4EeXfGyEjOndTLXL93Sp9ssTSWZva4O9Tk2p2j\nBgvvrJx00jJvkpWbDsC9+wmYKaywMnPShycci1uNk60flqYOaO4eZ9ORWTQLfBlHGx99OwcvLEXp\nXBdTuQWXb+7n72Nf0inkbf0JByr3Zvx97CvWHZpGkxqD0WoL2H3mV4yMjPFzbaxvR3P3BHJjU5TO\n9Z7e2AkEj4mJsYK6/i2o69+CAa3fZP7aD1m682tCm48wWDiVJHrfz6w5EMWIrhH4uFTHTGHB79tm\nkZ6ZbCBnpni0d91g36acTjhALZ9G2Fo64mqvBKC2TxNOJxzkQVYa5qaWVNN5NAB8HT2BtMxkRnSZ\ngrOtJyZyBVN+G0J+geHOp5mJ4YLd3702P4/bxbG43Zy8uo95qyfi51qTqS//9ki6FiE3NtHr+TQw\nV1gyd+QaziQc4sSVPazY+yN/bJ/D7NeiMTOpeByrkkyvUFtIw+rtCO/0kYFRAzDwDKkKDtaufDWq\neEFpVSI3wONQ+pkrecJBoW4hPvqFz8oYXh6GqcJC0lMmw87SycCIUty3YbJKqW+pzwvXjzFn5XgG\ntRlHiKoVlmY2HLqwlUVbZzyy/o9DSYOzcZlkmjLkxvIy8oXa0uvO8mlV+wV+3TyN37d9QaugF8sN\nLSrUaukYMoBeTYeXeWYcrYvDNo3L0aNI9dBIlQWwCdiCFId/B3BGCiEoP3O0IUVH2o8CHv04ELgL\nPJaLXnRE3FJgaWikyhkoOqHgXaSQgiKZr4CvQiNVbkAK4Id0wkFJmQ9DI1UfIXkWJCHlQKCkjA4H\n3XXBIyKMAwKBAJA8BoYPH0NW1g6ysupQUPAm9wt3MXtlR5CBqdwSJxtfBrSabXgkYYkXKFMTCwa3\n/YZ1h6fxw8ZBONv6067OaJbterfCEw6KSEg6yg8bBxmU1VJ2ZECrWQxpP59tJ+az9mAkGdnJWJo5\n4u1cT59UD0BuZErroBGs2jeJtMzbKJ3qMLDVXP31QGU7ujf8gH3nFrHpyCzsLN15ofEkqnu20ssU\n5R24l56Au71krfdyqoORTI7Sua7By4mWQjbEziQ98zYKEwv8XZvQpcG75Y5Lw2r90CIZCMLafIW/\nW5MyL56r9k8h4c4RxvfawFOnRF8B7s15qe237Dz9E/vP/YaRTI6jjTf1/HtV0gA0COjL0biVNA18\nSV924UYM0fsjpPZlMtYekmJX2waP0ntQ3E2PZ+vxeWTnpmNn5UHroJE0K9EGwI17p4k59QO5+Zk4\n2fjSs8nHBqECTja+DG47j5iTP7Bgy1BAhpt9DV5u9x02Fi56udPxm6jj273KoQ8CwdOiyPPK19cX\nZ2fDBJ5eTgEUFBaQm5+DuUKO3NiEQq3hZt45zREaVW9v4L6fmByPlZnNY+kT7NuU79dP5silGGr7\nFBvSgnyb8MumSO5nplBT2RBjo+KdyHOao7zWLUKfBT/1wV1SHjzau7WZwoJmNbvQrGYX2tXtw/sL\n+nIzOR53B9/H0r8iLt4wPK77wvWjeDmpKpCWjMfBvk0J9m3KoDbjGDanMbEXt9Op/kAcrF05pzli\nEE5x7losSicpBMLLWcWOkyvJycvWL3wvXD9m8B2ucqvN3nMbcbL1MBjLJ8HYyFi/s/2ssbN0wsHa\nlZvJCQaJAh8FGbInMuac1xzF0cZNnzgT4E7a9Sq3Izc2obDQ8O9J6aRi+/G/yM7N1HufnNMcAbR4\nOVf8vDwp5qZWNKvVlZ0nownvVH5CZpV7bTRJl57UEBYIOAKToiPiEgBCI1VBGO76HwNeCo1UyaMj\n4vJLVo6OiLsTGqlKBAKiI+IW8+gcA5xDI1UO0RFxyQ+VLofoiLgknb6vIu3ul3H9iY6Iu6WTGYwU\nKnC01HUtcLOEzP7oiDi9e4fOu8C0dD1B5QjjgEAgACA+Ph6FwpesLMn9cc1P3xKztD5bt/5Io0YV\nH731yWDDlzQvp2Be77ZU//m8ZgcymRH2VhX/APZu9im9m31a4XWF3IJuDd6jW4P3yr1ez78n9fyl\nl+kaXm0rbKdhtX40rNavwuuAQVw7gNxYweRBB8vIdW/4QYVt2Fl5lBmXRtX606hacXxuaSNA6oNE\nAtxbVKpbET0alR9rXHIcivB1bVhGF5V70yp7MNTz78mes79y7c4xvbdGef2VplPIODqFjKtUpk/z\nzx7av79bE70XSHlkZCdzVrONUV2XPLQtgeBZUOR5ZWHjTc3u9+je5CXCQl/B3NSSy4knid7/M3X9\nm+tdo11svTh7LZY2wb2QGyuwsbDHw8GXvWc3cO5aLNYW9mw4/Du3UzRYPSTOuSKCfJuSl5/LlqNL\nebXzJH15TWUDbqdoSHmQxIAS8d4AHo6+7Dy5mmoedcnOzeS3bTP1WeMrY82BX7G3csbPrRbGRsbs\nOrUaCzNrHHVJ59Yf+o2NsX/w7ZgtlbZTUFhA6oO7ZcpLZv2/cP04K/f+SLOaXTgVf4Cdp1Yb5CQo\nSezFHdxKSaCWT2Osze04eXUf2bmZKHX5D0Kbj0Ad8zXuDj76hITnNEf0CRlbB/Vk8fY5fLNmIgNb\nj+Ve+m3+2vO9QR/dGr3M38eWM+uvsfRpMQpbCwdupVxj79kNvNp5UpmwiH8jg9qO45dNkViYWtOg\nWlsKCvKIu3WG5PTbTxTO8DA8HP1ITr/NzlNrCPQK4WjcLvacXvfwiqVwsfPiWNwubty7irW5HZZm\n1rQO7sXSnfP4KnoCYW3H8SArjR/WT6Fpza7P3PAy5oVpDO882SCJZ0l6Nx/FB7/244f1U+jcIAxz\nhSXX78YRe2k7o3s8/DdRxzUgBxgbGqmaD9QCIkvJfIfkGfBnaKRqGtJOfCPgbHRE3EngY2CeLjZ/\nA1ICv/qAZ3RE3AzK5xiSl0JLpBMBAAiNVLki7eTXQPJvqB0aqbIHrkVHxKXoZN5A8lK4D3RGOkVh\nYnREXHqJdiYgeUQUAn2RjjDsX5TjIDRS5Yh09GEM0uL/VZ1c61J6tgKuREfExVU8hILSCOOAQCAA\nwNfXl9zceKQTbuoAJ8nLS8DX17dK7Ry/shZ7K09sLdy4nXqJTUdnUcOrjT7mXFCW7LwH3LufYBDi\n8G9Dbqygd7PPyMpNe96qlEtqRiIvNPqo7JGSAsE/QEnPq+zcQO4mTGGLYgmnUzdRUJiPg40rbYJ7\n0a/EQnxw2/F8v2EKr3/TjvyCPFZOuUT/1m9yJ+0Gn6qHo5Cb0b5uX9rWCUWTdElfryru7s62Hrja\nK7mTesMgb4GZwgKVexCXEk+WSUA4tucXfL9uEhN+CcXB2oVBbcaRnpliIFOeDuYKS1bt/5lbyQmA\nDH+3Wnw8OEqfvf5+ViqJ9+IfqnPivSu8+mWxTlpdHPZfky9gpDvBpFfT4cTfPs/y3fMxV1gwuO3b\n+kR6YOgybmlmzcELf7N893xy8rJws/fmzRc/J1ApecC90PgVnRHkC1If3MXTyZ/3+3+Hj0sN/VhN\nDlvADxum8O7PvfB0UjGsw/tMXzZS34eDtQufhy/n9+2z+XTJq+Tm5+Bs60E9/5aYyCs2rFRlLp81\nnUIGYK6wYNW+n/lj+2xd8sVqj5QnojLKu8eS89OoentCm4/g1y2fkZuXQz1VSwa3e5sfN3xcpX46\n1x/ImYSDTPg5lJy8TP1Rhh+/FMWCzZ8xcUFfFHJTGtfoxIgukx/jRqombmKswMS84rn3da3BtFfU\nLN4xl8mLBlOoLcTNXkmTGsVJNR/2fERHxN3VxfBPB8YgvcC9jbSwLpJJDI1UtQZmISUE1AKngJG6\n6wtCI1UPgPd07WQBZ5Bi/ivqtzA0UhWF7kjBEpdeRzI2aHX/FVl5woGi+KLGSLkBrJBOWHgtOiKu\ntFW/G/AR0sL/BNAzOiKutFVxKJJhQYZkbGgTHRF3pJRMGPBTRfchKB/Zk8Tr/JPIZDLt/4quAsH/\nKkU7XyYmPuTlJbBgwXeEhQ2sUht7zi7k8MVlPMi+h5WZI9U9W9MpZBwK+b9/50QgEAgeh8OHD9Op\n0+ukpRW/m9rYPNzzSlB1Rs5rQ49GQ+nVbPjzVkUgeC7o8i08V8uSLmfAGaBRUUjDv4nQSFVtpJMM\nqkdHxN1/3vr8LyE8BwQCgZ6wsIF07Ni+wpjZR6FlrVdoWeuVp6+cQCAQ/Et5Wp5XAoFA8L9AdERc\nki5fgDfwrzMOAB7AUGEYqDrCOCAQCAxwdnZ+LKOAQCAQ/FdxdnZmwYLvGD68nYHnlfguffqUPdZU\nIBA8D6Ij4qqeHOIfIjoirvyzTQUPRYQVCAQCgUAgEDwFKjutQCAQCJ4G/4awAsH/X4RxQCAQCAQC\ngUAgEAj+BxDGAcGzxOh5KyAQCAQCgUAgEAgEAoHg+SKMAwKBQCAQCAQCgUAgEPzHEcYBgUAgEAgE\nAoFAIBAI/uMI44BAIBAIBAKBQCAQCAT/cYRxQCAQCAQCgUAgEAgEgv84wjggEAgEAoFAIBAIBALB\nfxxhHBAIBAKBQCAQCAQCgeA/jjAOCAQCgUAgEAgEAoFA8B9HGAcEAoFAIBAIBAKBQCD4jyOMAwKB\nQCAQCAQCgUAgEPzHEcYBgUAgEAgEAoFAIBAI/uMI44BAIPhPszZ8GWuGLX3eajw2+Tn5zLKdwpXN\nF/6R/u6ev8P3NWaSl5n7j/RXVRY2/5YrWy4+bzUEAoFAIBAI/ueQabXa563DIyGTybT/K7oKBIKn\nxyzbKchkUN6fv0wGtQeH0O27Po/dfu79HLRaLaY2Zk+g5dMl+VISCxrOI/zgWJwCXfTla8OXoS3U\n0nPRIAP5jKQHmNmZY2xi/Mx1WznwD7xa+NL4rZYA5GbksvXdtdw+kUjyhSR82gXQb8XQMvVi5+/j\nxK+HSL+ehq2PPc3fb0dg32D99YLcfPbPjOHsnyfIuHUfx5outInsgk8blUE79xPT2fXxZq7+fYm8\nrDzs/B3oMi8Uj0ZKAC5En2bfjB2EHxj7DEdBIBAIBILng0wmQ6vVyp63HoL/n8iftwICgUBQGWMu\nv6//d9zG82x5azVjLn9AkbHQxMyk3HqF+QUYyR++WFZYmz4dRZ8iWq1k+HhULJ2tnp0yJUiJu0f8\n9st0+TZUX6bNL8TEQkGD0c24vO4chQVlrTix3+5l3xc76Pptb1zrenDjQAKb3lyFuaMFPm2lxX/M\n5M1cXn+WLt/0xs7fgbiNF1g18A9e3jkaxxqSgSTrXiZLOv2Ib8dq9Fs1DHNHC1KvJGPuYKHvK6BH\nTbaMW821XVfwbu3/jEdEIBAIBAKB4P8PwjggEAj+1ZRc+JramgNg4WRpIFO0095z0UCO/XSAm0du\n0HH2C/h3rcH299Zz/UACOalZ2Po60OSd1tQaUFdft/Ru/B8dfsSjsRIjuRGnfjuCscKYoCH1af1x\nZ32d8ytOsX9WDKlXk5GbyXGp406v38Iwszdn1ydbSNgRR9DL9Tk4ZyfZKVn4d6lBl3mhmNpK3gna\nwkL2Tt/O6cVHybybiWMNZ1pFdMK/c3Xyc/L5tdE8ZDKIavINMhn4dqiGS7Ab51eeRiYr9qZ4aeso\nnIPd+NJlKn2XD8G/Sw39WPRWD+bId/u4GXsdW18HOs5+AWVLP/09XFp3lp1TNnP/RhoeTbwJeqk+\nG0atYMzl9ys0NpxfeQrXeh6l5sSMTnNfBODmYQ33E++XqXd2+QlCRjShes/aANj62JN4SMPBL3fp\njQPnlp+g9dTO+LYPAKDB6GbEb79M7Px9dJknGSP2z47BPsCJLl8XGydsve0N+jI2McavYzXO/XVS\nGAcEAoFAIBAIqoAwDggEgv837Prkb9pN74pzkBvGpnLys/LwaKyk6XttUFiZcuXvi2wcvRJbH3s8\nm3hX2M7pxUdp/FZLhux4nZux19kwagXuDZVU61GTdE0q61/7k/Zf9EDVpQa5D3K4cfCaQf3ky3e5\nvO4cfVcMIyc9m81vrGLLuNW8uHAgAAe/3M2xnw7SeV4vXILcOPn7EVaFLSb8wJs4VHMmbPMIlnb9\nhbBNw7EPcEKukGNkYsS9i0lQqKXLt73RarWYO1hQmF9Y7j3sjtxK28+6YufnwN5p21j76nJGnX4X\nY4WclLh7rBm2jMbjWhI0OITbJ28SM2njQ70Vru9LwC3Es2qTAhTk5GNsavhzY2wmJ/GQRv85Pzcf\nY4Whp4fcXM6N/Qn6z5fXnyOwTzCrX1ZzfV88Vh421Hu1MXXDGxnUc2/gxbFfDlVZT4FAIBAIBIL/\nMiIhoUAg+H9Do7EtCOheE1tve6xcrbH1tqfBmOY413bD1seekBFN8O9SnfN/nay0Hde6HjSd0BZ7\nf0dqDaiLZxMl12LiACnmXauF6r1qY6O0w6mmK3VfaYSZvbm+fkFuAd1/7odzLVe8mvrQcc6LXIg+\nzf0baQDEfrOXpu+1oUZoEPYBTrSZ2gXXeh4c/mYvABaOkpu8mb0Fls5WmNqaYWKhQG4Yhk5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M1p59WKGf3HYG1hefUlTwPNfygF4W8QCQkFQRAEQRD+gzwcXckrKWx2X26xkiOx4bwx6XGCPPxw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OX8ungiCY9Dmf39EbXP8wn52RlqSRcJCQkJCQkJiZeBF1WtQEJCQuJ/lpSUFK5cuUJKSkqV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\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qSlfyck0BsMb6BnLOZzDlrjta7T+stzfb16XhE+CEStXGeB5XkXW2jMqyeh7/2zD8Ak1d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\no4+iaKn7+D29gr5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1C3kW6MueE/tYuXM1vVp2/+R4K7pVoGQRV3qN78dpr7MEvArk+t0b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\n6perWlRWrrpG/zxBUQFh8nbxKLHd2exMjVsyWe2rNVWId4A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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff4ee0c57d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "start=50; end=100\n",
    "X = fac0[start:end]\n",
    "Y = fac2[start:end]\n",
    "plt.figure(figsize=(15,15))\n",
    "plt.scatter(X, Y)\n",
    "for i, x, y in zip(topMovies[start:end], X, Y):\n",
    "    plt.text(x,y,movie_names[movies[i]], color=np.random.rand(3)*0.7, fontsize=14)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  Neural net"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Rather than creating a special purpose architecture (like our dot-product with bias earlier), it's often both easier and more accurate to use a standard neural network. Let's try it! Here, we simply concatenate the user and movie embeddings into a single vector, which we feed into the neural net."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "user_in, u = embedding_input('user_in', n_users, n_factors, 1e-4)\n",
    "movie_in, m = embedding_input('movie_in', n_movies, n_factors, 1e-4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "x = merge([u, m], mode='concat')\n",
    "x = Flatten()(x)\n",
    "x = Dropout(0.3)(x)\n",
    "x = Dense(70, activation='relu')(x)\n",
    "x = Dropout(0.75)(x)\n",
    "x = Dense(1)(x)\n",
    "nn = Model([user_in, movie_in], x)\n",
    "nn.compile(Adam(0.001), loss='mse')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 79838 samples, validate on 20166 samples\n",
      "Epoch 1/8\n",
      "79838/79838 [==============================] - 2s - loss: 2.5208 - val_loss: 0.8969\n",
      "Epoch 2/8\n",
      "79838/79838 [==============================] - 2s - loss: 1.4826 - val_loss: 0.8493\n",
      "Epoch 3/8\n",
      "79838/79838 [==============================] - 2s - loss: 1.2283 - val_loss: 0.8461\n",
      "Epoch 4/8\n",
      "79838/79838 [==============================] - 2s - loss: 1.0386 - val_loss: 0.8128\n",
      "Epoch 5/8\n",
      "79838/79838 [==============================] - 2s - loss: 0.9115 - val_loss: 0.8000\n",
      "Epoch 6/8\n",
      "79838/79838 [==============================] - 2s - loss: 0.8452 - val_loss: 0.7966\n",
      "Epoch 7/8\n",
      "79838/79838 [==============================] - 2s - loss: 0.8179 - val_loss: 0.7938\n",
      "Epoch 8/8\n",
      "79838/79838 [==============================] - 2s - loss: 0.8030 - val_loss: 0.7943\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x7ff5134a8450>"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nn.fit([trn.userId, trn.movieId], trn.rating, batch_size=64, nb_epoch=8, \n",
    "          validation_data=([val.userId, val.movieId], val.rating))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This improves on our already impressive accuracy even further!"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [default]",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
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